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Canadian Physics Counts: Considering How Identity Relates to Experiences of Harm within the Canadian Physics Community

Published 24 Mar 2026 in physics.ed-ph and physics.soc-ph | (2603.24622v1)

Abstract: Harmful experiences such as harassment and discrimination continue to push many people out of science. To better understand identities and experiences of harm among physicists, we conducted Canadian Physics Counts, the first comprehensive national survey examining equity, diversity, and inclusion within Canada's physics community. To better understand identities and experiences of harm among physicists, we conducted Canadian Physics Counts, the first comprehensive national survey examining equity, diversity, and inclusion within Canada's physics community. We explored experiences of harm focusing on personal harassment, sexual harassment, and sexual assault. We measured both direct experiences of harm and awareness of harm happening to others. Our analyses revealed that women and gender-diverse physicists reported experiencing personal harassment at twice the rate of men, a pattern consistent across all academic positions, including students and early-career researchers. An intersectional focus revealed even deeper inequities. Black women and men reported the highest rates of personal harassment, while Indigenous women and men faced elevated levels of sexual harassment. Physicists with disabilities were disproportionately affected. Disabled women and gender-diverse respondents reported the highest rates of personal and sexual harassment and sexual assault, and disabled men experienced more personal harassment than men without disabilities. These findings are a clear call to action to the physics community to confront racism, sexism, homophobia, and ableism so every physicist can thrive and contribute to solving society's greatest challenges.

Summary

  • The paper quantifies the disproportionate incidence of personal, sexual harassment, and assault among marginalized physicists using a national survey of 1968 respondents.
  • It employs robust non-parametric statistics to reveal significant intersectional disparities across gender, race, disability, and sexual orientation.
  • Findings underscore the need for targeted, intersectional policies to mitigate harm and improve retention in Canadian physics.

Identity, Harm, and Intersectionality in the Canadian Physics Community: An Analysis of the "Canadian Physics Counts" National Survey

Introduction

The systemic impact of identity-based harm in physics undermines both individual well-being and the advancement of the discipline. "Canadian Physics Counts: Considering How Identity Relates to Experiences of Harm within the Canadian Physics Community" (2603.24622) presents the first intersectional, comprehensive empirical analysis of harassment, discrimination, and exclusion experienced and observed by physicists in Canada. Drawing from a national survey (N = 1968 respondents), the work systematically quantifies frequency and modality of harm across the axes of gender, race, disability, and sexual orientation, with an explicit intersectional statistical framework. The study thereby fills a significant gap in North American EDI research, which has historically focused on the US and often failed to analyze the multiplicative effects of overlapping marginalized identities.

Methodological Approach

The study employs robust quantitative analyses, utilizing gender (women, men, gender diverse), race (BIPOC vs. White, with partial disaggregation into Black, Indigenous, POC, and White), disability (disabled vs. non-disabled), and sexual orientation (sexually diverse vs. heterosexual), with intersectional groupings (e.g., BIPOC women, disabled men, etc.) wherever sample size permitted. Harm was operationalized along three axes: personal harassment, sexual harassment, and sexual assault, with survey items designed to distinguish between direct experience and awareness (vicarious exposure), following validated protocols (e.g., Cech and Waidzunas, 2019). Non-parametric statistics (Kruskal-Wallis with post-hoc Dunn-Bonferroni) enabled significance testing despite group size imbalances. The survey achieved wide distribution across Canadian institutions and physics subfields.

Key Findings

Gender and Harm

Women and gender-diverse participants reported experiencing personal harassment at twice the rate of men. Sexual harassment prevalence was 6x higher in women and 5x higher in gender-diverse respondents than in men; sexual assault was 5x higher for women than men. These rates of harm were statistically robust across all academic strata, including undergraduate and graduate students, postdocs, and faculty. Gender-diverse respondents reported the highest awareness of all forms of harm, potentially indicating heightened exposure or sensitivity within “whisper networks.”

Intersectionality: Race, Disability, Sexual Orientation

A core contribution is the explicit intersectional quantification of harm:

  • Race: While aggregated BIPOC status did not predict elevated harm compared to White counterparts, descriptive and partially disaggregated analyses revealed that Black women and men, and Indigenous women and men, reported the highest levels of personal and sexual harassment. For instance, 1.5x as many Black women as White women reported at least one incident of personal harassment; among men, the incidence of sexual harassment was nearly 5x higher in Indigenous men than White men. These findings, despite sample size limitations, strongly suggest that aggregation into BIPOC conceals differential patterns of harm within racialized populations.
  • Disability: Disability status was associated with high harm prevalence. Disabled women and gender-diverse respondents reported the highest rates of personal and sexual harassment and sexual assault; disabled men experienced more personal harassment than non-disabled men. Statistically, disabled women were at substantially higher risk than all other groups across all forms of harm.
  • Sexual Orientation: Women experienced more personal and sexual harassment than men, regardless of sexual orientation. While no significant differences in direct experiences of harassment by sexual orientation emerged, sexually diverse women showed descriptively higher risk for sexual harassment.

Harm by Academic Position

Analysis by position confirmed elevated harm among women at every career stage, including students, postdocs, and faculty. Very high personal harassment rates among women assistant and associate professors (up to 78%) underscore the persistent risks of attrition and hindered advancement.

Awareness of Harm

Awareness of harm was pervasive and often exceeded direct experience: over half the sample indicated awareness of personal harassment at least once. Gender and intersectional lines mirrored experienced harm patterns, with those most marginalized also most likely to hear about, witness, or be exposed to others’ harmful experiences. The data substantiate extra-individual impact vectors for harm in physics, with secondary witnesses incurring psychological risk.

Implications

Scientific and Institutional Consequences

  • Retention and Attrition: The results provide direct empirical support for the assertion that marginalized physicists, especially those with intersectionally minoritized identities, are disproportionately vulnerable to harm likely to drive attrition from physics. The burden is substantial even among trainees, indicating early leaks in the pipeline with direct consequences for disciplinary diversity and creative potential.
  • Policy Precision: The findings demonstrate the necessity for EDI interventions and policies that are intersectional by design, acknowledging compounded risks for those marginalized along multiple vectors. Aggregating across BIPOC or “diverse” categories masks underlying gradients of harm and impedes targeted remediation.
  • Culture and Leadership: The data expose a discordance between actual harm prevalence and its recognition among majority-group physicists, especially White men, reinforcing that unrecognized harm functions to preserve the status quo. This amplifies the call for institutional accountability and cultural change, not merely policy or compliance box-checking.

Theoretical Contributions

  • The work empirically validates key claims of intersectionality theory (Crenshaw, 1991) in a physics context. Specifically, it identifies that additive or multiplicative risks are context- and identity-dependent; race and disability interact with gender in non-additive ways, demanding nuanced analytics.
  • The explicit modeling of both direct experience and awareness of harm as analytically separable but equally consequential variables expands the scope of climate studies by incorporating the effects of vicarious trauma and informal knowledge networks.

Limitations

Self-selection and single-item measures introduce risks of response bias and underestimation of specific identity-based harms (e.g., microaggressions). Small sample sizes for the most marginalized groups limited statistical power for some intersectional analyses, and grouping for power obscured distinctions within BIPOC and disabled subpopulations. Nonetheless, strong effects were observed where sufficient data permitted, and the methodology is consistent with current best practice in equity climate studies (2603.24622).

Future Directions

  • Data Disaggregation: Large-N, demographically targeted recruitment is necessary to enable robust disaggregation and examination of intersectional subgroups (e.g., disabled Black women, queer Indigenous men).
  • Qualitative Deepening: Richer qualitative studies and behavioral checklists (rather than single-item omnibus questions) will enable more granular mapping and reduce cognitive denial/avoidance effects in self-reporting.
  • Longitudinal Retention Studies: Prospective research is needed to track the longitudinal impact of experienced/awareness harm on career advancement, retention, and output, directly quantifying cost to Canadian physics.
  • Programmatic Interventions: Research evaluating intervention efficacy, ideally via randomized or quasi-experimental designs, is needed to translate findings into concrete systemic change.

Conclusion

This study delivers the most comprehensive intersectional quantitative assessment of harm in Canadian physics to date, establishing empirically that compounded marginalization—by gender, race, disability, and sexual orientation—predicts substantially elevated harm. Both experienced and witnessed harm are pervasive, with implications for mental health, sense of belonging, and retention of talent. These findings underscore the necessity for intersectionally-informed, datadriven policies and cultural reforms in Canadian physics to prevent ongoing loss of diversity and innovative potential. The study’s framework will inform future theoretical models of harm in STEM, and its findings demand actionable commitment from institutions and individuals in positions of privilege and power.

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Explain it Like I'm 14

A simple explanation of “Canadian Physics Counts”

Overview of the paper’s main topic and purpose

This paper looks at safety and fairness in the Canadian physics community. The researchers wanted to understand who is experiencing harmful treatment—like bullying, sexual harassment, and sexual assault—and how often it happens. Their goal was to use a big, nationwide survey to show where problems are and who is most affected, so physics in Canada can become safer and more welcoming for everyone.

Key objectives and research questions

Put simply, the researchers asked:

  • How often do physicists in Canada experience harm (bullying, sexual harassment, sexual assault)?
  • How often do they hear about harm happening to others?
  • Do these experiences differ by identity, such as gender, race, disability, and career stage (student vs. professor)?
  • What happens when identities overlap (for example, being both a woman and Black, or being disabled and gender-diverse)?
  • How do patterns of harm vary across different levels of the physics pipeline (undergrad, grad student, postdoc, professor, industry)?

Research methods explained in everyday language

The team created a large online survey and shared it widely through physics organizations, university departments, labs, and professional groups across Canada. People could take part if they study physics or work as physicists in Canada. The survey was available in English and French.

They asked two kinds of questions about three types of harm:

  • Personal harassment (bullying, intimidation, discrimination)
  • Sexual harassment (unwanted sexual attention or comments)
  • Sexual assault (non-consensual sexual contact)

For each type, participants answered:

  1. “I have experienced this at my school or workplace.”
  2. “I know someone else who has experienced this at my school or workplace.”

People rated how often from 1 (never) to 5 (frequently). Some chose to skip questions, which is common for sensitive topics.

Because some groups had small numbers (for example, Indigenous physicists), the researchers sometimes grouped identities into broader categories to protect privacy and make sure results were statistically meaningful. For example:

  • “BIPOC” grouped Black, Indigenous, and People of Colour.
  • “Gender-diverse” grouped identities like non-binary, genderqueer, and agender.
  • “Disabled” grouped people with any self-reported disability.

How they compared groups in simple terms:

  • Imagine you write all the responses on slips of paper and rank them from “never” to “frequently.” Then you check whether one group’s slips tend to show higher ranks than another’s. That’s similar to the non-parametric tests they used (like Kruskal–Wallis and Mann–Whitney), which are good for comparing groups without assuming the data follow a perfect bell curve.

Main findings and why they matter

The big picture: Harm is common, and it doesn’t affect everyone equally. Women, gender-diverse people, Black and Indigenous physicists, and disabled physicists reported more harm.

Key takeaways include:

  • Across gender:
    • About twice as many women and gender-diverse physicists reported personal harassment compared to men.
    • Women reported sexual harassment about 6 times as often as men; gender-diverse participants reported it about 5 times as often as men.
    • Women reported sexual assault about 5 times as often as men.
    • Gender-diverse participants were the most likely to be aware of harm happening to others, especially sexual assault.
  • Across career stages (students to professors):
    • Women reported more personal and sexual harassment than men at nearly every level (undergrad, master’s, PhD, postdoc, assistant and associate professor).
    • Some stages showed striking rates. For example, a high share of assistant and associate professors reported personal harassment. Many gender-diverse PhD students reported personal harassment.
    • Professionals (like professors and industry physicists) reported more harm than students, but students—especially women and gender-diverse students—also faced sexual harassment and assault.
  • Race and intersectionality:
    • When comparing big categories, women of any race reported more harm than men of any race.
    • Looking closer, the patterns were even more concerning:
    • Black women and Black men reported the highest rates of personal harassment.
    • Indigenous women and Indigenous men reported elevated sexual harassment rates.
    • These findings matter because they show certain groups face unique and intense pressure, which can push them out of physics.
  • Disability:
    • Disabled women and gender-diverse physicists reported the highest rates of personal and sexual harassment, and sexual assault.
    • Disabled men also reported more personal harassment than men without disabilities.
  • Awareness vs. experience:
    • Many people had at least heard of harm happening to others (awareness). This “whisper network” suggests problems are widely known even when not officially reported. Awareness itself can affect mental health and sense of safety.

Why this matters:

  • Harm in school or the workplace damages mental and physical health, lowers confidence, reduces sense of belonging, and causes people to switch fields or leave science. Physics loses talent and creativity when people are pushed out.

Implications and potential impact

This research is a wake-up call for the physics community in Canada. It shows that:

  • Racism, sexism, homophobia, transphobia, and ableism are real and widespread.
  • Harm affects careers, learning, and well-being—and it hits certain groups hardest.
  • Policies and programs must be intersectional. That means solutions should consider overlapping identities (for example, the experiences of disabled women or Indigenous men), not just single categories.
  • Institutions should strengthen and enforce clear anti-harassment policies, improve reporting systems, provide regular training, and ensure accountability.
  • Physics organizations should collect better, more detailed data—especially for small groups like Indigenous and Black physicists—so they can see problems clearly and track progress.
  • The ultimate goal is to build a physics community where everyone can learn, work, and contribute safely. That’s good for people and for science: diverse, respected teams solve harder problems and make stronger discoveries.

In short, the study shows who is being harmed and how often. It explains why those harms matter and calls for action to make physics in Canada a fair and welcoming place for all.

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Knowledge Gaps

Knowledge gaps, limitations, and open questions

Below is a consolidated list of what remains missing, uncertain, or unexplored in the study, framed as concrete, actionable items for future research.

  • Sampling and generalizability: Use probability-based or stratified sampling (and/or post-stratification weighting) and report response rates to improve representativeness beyond a convenience sample distributed via professional networks.
  • Nonresponse and skip bias: Investigate and adjust for differential skipping of harm items (e.g., higher skipping by men and BIPOC respondents) via follow-up probes, sensitivity analyses, or Heckman-style corrections.
  • Survivorship bias: Include people who left physics (e.g., alumni, former employees, “leavers” studies) to avoid underestimating harm that contributes to attrition.
  • Exposure-time confounding: Collect and control for time-in-field/years in role to separate “once or more” prevalence from longer exposure among professionals relative to students.
  • Cross-sectional design: Implement longitudinal designs to establish temporal links between harm, mental health, belonging, productivity, and retention/exit from physics.
  • Single-item measures: Replace each harm type’s single item with validated, multi-item, time-bounded scales (e.g., past 12 months; SEQ variants) to capture severity, frequency, and subtypes.
  • Timeframe ambiguity: Specify and standardize a recall period (e.g., 12 months vs. lifetime at current institution) to improve comparability and interpretability.
  • Lumping of “personal harassment”: Disaggregate bullying/intimidation by basis (e.g., race, gender, disability, sexuality) and by behavior (e.g., exclusion, verbal abuse, microaggressions) to identify targeted interventions.
  • “Awareness of harm” construct: Differentiate witnessing, first-hand disclosures, and third-hand rumors; capture recency, relationship to victim, and unit/location to reduce misclassification and double counting.
  • Perpetrator and context data: Collect perpetrator role (peer, supervisor, student, external), power differentials, setting (classroom, lab, conference, online), location, and event circumstances to identify leverage points.
  • Reporting and response: Measure reporting behaviors, barriers, institutional responses, outcomes of reports, and trust in reporting systems to connect prevalence with policy effectiveness.
  • Intersectional resolution: Increase sample sizes (via oversampling/targeted recruitment) for Black, Indigenous, gender-diverse, disabled, and sexually diverse physicists to enable fully disaggregated, intersectional analyses.
  • Racial aggregation: Avoid collapsing all racialized groups into “BIPOC”; analyze Black, Indigenous, and other racial/ethnic groups separately to reveal distinct patterns masked by aggregation.
  • Gender identity granularity: Distinguish cisgender and transgender identities across men and women; avoid grouping all non-cis identities into a single “gender diverse” category when possible.
  • Sexual orientation measurement: Avoid collapsing into “sexually diverse” vs. “heterosexual”; analyze specific identities (e.g., lesbian, gay, bisexual, asexual, pansexual) and their intersections with gender and race.
  • Disability measurement: Move beyond a binary disabled/not disabled; capture type, visibility, severity, accommodation status, and accessibility barriers to pinpoint mechanisms and remedies.
  • Geographic and linguistic variation: Examine differences by province/territory, urban vs. rural, institution size/type (e.g., U15 vs. smaller universities, national labs, CEGEPs), and language (Francophone vs. Anglophone).
  • Subfield and sector differences: Analyze variation by subfield (e.g., astronomy, medical physics, condensed matter) and sector (academia, industry, government labs, teaching) to tailor interventions.
  • Institutional climate and policy context: Collect institution identifiers (confidentially) and policy indicators (e.g., Scarborough Charter signatory, EDI infrastructure) to model institution-level effects using multilevel methods.
  • Statistical reporting: Report effect sizes and confidence intervals (not just p-values); consider false discovery rate controls instead of overly conservative Bonferroni corrections; pre-register analysis plans.
  • Clustering and dependence: Account for clustering within institutions/departments in analyses (e.g., multilevel models) to avoid inflated Type I error from non-independence.
  • Qualitative depth: Conduct a systematic qualitative thematic analysis (beyond illustrative quotes) to surface mechanisms, context, and nuanced intersectional experiences.
  • Measurement equivalence: Test and document cross-language (French/English) measurement equivalence and translation validity; assess whether items function similarly across demographic groups.
  • Online and hybrid environments: Measure harassment in virtual spaces (email, videoconferencing, chat) and at conferences/fieldwork, which may have distinct risk profiles.
  • Protective factors: Identify moderators (e.g., mentorship, bystander training, lab culture, unionization, departmental leadership) that buffer harm and promote belonging and retention.
  • Policy evaluation: Use quasi-experiments or pre/post designs to assess the impact of specific policy changes (e.g., reporting procedures, mandatory training) on harm prevalence and outcomes.
  • Basis attribution: Ask respondents to attribute perceived bases for harassment/assault (e.g., gender, race, disability) to connect experiences with targeted anti-racism, anti-sexism, anti-ableism interventions.
  • Career-transition risk points: Examine harm during key transitions (e.g., BSc→MSc, PhD→postdoc, postdoc→faculty) where power differentials and precarity may heighten vulnerability.
  • Population benchmarking: Compare sample demographics with national physics workforce/student benchmarks (e.g., CAP, Statistics Canada) and apply weights to correct imbalances (e.g., 60.7% students).
  • Clarify category inclusions: Revisit inclusion of “polyamorous” under sexual orientation and ensure categories align with current best practices and community standards.
  • Data on immigration/visa/SES: Collect immigration status, international-student/visa status, and socioeconomic background to evaluate additional axes of vulnerability not analyzed here.
  • Harm severity and chronicity: Capture severity (e.g., threat, physical contact), duration, and cumulative exposure to assess dose–response relationships with health and career outcomes.
  • Assistant professor anomaly: Probe why gender differences in sexual harassment were not observed among assistant professors (e.g., small N, reporting hesitation, cohort effects).
  • Transparency in confidentiality: Detail how confidentiality/anonymity were balanced with collecting institution- or unit-level data to enable policy-relevant analyses without risking identifiability.
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Practical Applications

Immediate Applications

Below are actionable, deployable-now use cases that organizations and individuals can implement based on the paper’s findings and methods.

  • Academia (departments, institutes, colleges): Physics climate “pulse” surveys using the paper’s 6-item harm battery
    • What: Reuse the survey’s concise items on experienced and “awareness of” personal harassment, sexual harassment, and sexual assault as a standard departmental pulse once or twice per year; disaggregate by gender, race, disability, sexuality when sample sizes allow.
    • Why: The study shows these items are sensitive to identity-based risk and career stage; “awareness of harm” works as an early climate indicator tapping whisper networks.
    • Tools/workflows: Survey toolkit (question text, consent, routing), dashboard with intersectional breakdowns, privacy-preserving reporting templates.
    • Assumptions/Dependencies: Ethics approval; anonymity thresholds for small-N groups; leadership buy-in; secure data handling.
  • Academia (all levels): Targeted interventions for high-risk groups and stages
    • What: Prioritize harassment prevention, bystander training, and supervisor training for (a) women and gender-diverse students (BSc–PhD), (b) disabled physicists, and (c) assistant/associate professors, where the survey shows elevated harm.
    • Why: Findings show consistently higher harm for women and gender-diverse physicists at every position; professionals report more harm than students; assistant/associate professors report high personal harassment.
    • Tools/workflows: Role-specific micro-trainings, supervision agreements, mentoring circles for Black and Indigenous physicists, disability-centered accommodations playbooks.
    • Assumptions/Dependencies: Scheduling time; incentives for participation; accessible materials.
  • Professional societies and conference organizers (physics, astronomy, medical physics): Strengthen codes of conduct and on-site response
    • What: Enforce visible codes of conduct; deploy confidential reporting channels, trained responder teams, and clear sanction pathways at meetings and schools.
    • Why: The study links sexual harassment to attrition; women/gender-diverse respondents report disproportionate harm; awareness rates are high at events and workplaces.
    • Tools/products: On-site reporting app and hotline, trained volunteer “safety stewards,” post-event climate summaries.
    • Assumptions/Dependencies: Legal review; insurance; clear governance for sanctions.
  • Research groups and laboratories: Supervision and TA-student boundary safeguards
    • What: Written supervision compacts (expectations, escalation paths), TA–student interaction policies (e.g., two-person grade dispute meetings), lab meeting norms with anti-bullying guardrails.
    • Why: Qualitative comments describe coercion and abuse of power by supervisors and TAs; early-career roles are vulnerable.
    • Tools/workflows: Templates for supervision compacts; TA policy inserts in syllabi; grading audit trails.
    • Assumptions/Dependencies: Union/collective agreement alignment; chair-level enforcement.
  • HR and Ombuds (universities, labs, industry R&D): Confidential, multi-channel reporting and trauma-informed response
    • What: Offer anonymous and named reporting, third-party ombudsperson access, and survivor-centered response protocols aligned with the policy definitions used in the survey.
    • Why: Paper uses policy-aligned harm definitions and shows both experienced and witnessed harm are prevalent.
    • Tools/products: Case management system with privacy controls; survivor resources; clear timelines and outcome transparency.
    • Assumptions/Dependencies: Legal due process; confidentiality; trained case handlers.
  • Funding agencies (tri-council, foundations): Condition funding on climate monitoring and response plans
    • What: Require annual reporting of aggregate harm metrics (including “awareness of harm”), intervention plans, and progress; reward departments that reduce disparities for women, gender-diverse, disabled, Black, and Indigenous physicists.
    • Why: The paper provides a validated, minimal metric set and shows sharp inequities; funders already mandate EDI.
    • Tools/workflows: Standardized reporting templates; compliance audits; incentive grants.
    • Assumptions/Dependencies: Policy authority; harmonization across agencies; privacy safeguards.
  • Industry (software, energy, finance, aerospace, healthcare/medical physics): Adopt the harm/awareness survey for technical teams employing physicists
    • What: Integrate the survey into engagement cycles; track identity-disaggregated climate data; deploy targeted trainings for high-risk teams.
    • Why: Physicists work widely in industry; patterns of harm in physics are likely to generalize to R&D contexts with similar hierarchies.
    • Tools/workflows: HRIS integration; team-level dashboards; bystander and supervisor training.
    • Assumptions/Dependencies: Employee trust; compliance with local labor laws.
  • Education (undergraduate programs, high schools): Syllabus-level norms and micro-interventions
    • What: Include anti-harassment statements, grading dispute protocols, and reporting options in syllabi; first-year orientation on respectful lab/class conduct and bystander skills.
    • Why: BSc/MSc students (especially women) report elevated harm; early norms shape climate.
    • Tools/workflows: Syllabus language pack; 30-minute orientation modules; lab partner rotation to reduce isolation.
    • Assumptions/Dependencies: Curriculum autonomy; faculty adoption.
  • Accessibility (across sectors): Immediate accommodations and universal design in physics spaces
    • What: Fast-track accommodations for disabled physicists; audit labs/classrooms/conferences for accessibility; provide remote participation options.
    • Why: Disabled women and gender-diverse respondents report the highest harm; structural ableism was noted.
    • Tools/workflows: Accessibility audit checklist; centralized accommodations portal; captioning/interpreter provisioning.
    • Assumptions/Dependencies: Budget; facilities coordination; vendor capacity.
  • Communication and culture: Normalize climate transparency without doxxing
    • What: Share aggregate, de-identified climate metrics (including “awareness of harm”) and response actions each term.
    • Why: Awareness of harm is a climate signal; transparency builds trust and accountability.
    • Tools/workflows: Semiannual climate brief; anonymization thresholds; Q&A sessions.
    • Assumptions/Dependencies: Robust anonymization; leadership willingness to publish.
  • Individual daily practice (students, staff, faculty): Low-burden personal protections and allyship
    • What: Documentation kits (timestamped notes, secure email-to-self), buddy systems for contentious meetings, structured bystander scripts (name the behavior, state impact, redirect).
    • Why: The paper evidences power-abusive scenarios and widespread microaggressions; individual readiness reduces harm and improves reporting quality.
    • Tools/workflows: One-page bystander cue cards; meeting buddy sign-up; secure note-taking templates.
    • Assumptions/Dependencies: Safety judgment; awareness of local policies.

Long-Term Applications

These require further research, scaling, or development before broad deployment but are natural next steps implied by the study’s findings and methodology.

  • National, longitudinal physics climate observatory
    • What: A multi-year panel tracking experienced and awareness-of-harm metrics, retention outcomes, and intervention effects across Canadian physics institutions.
    • Why: The cross-sectional survey established disparities; longitudinal data can quantify attrition risk and intervention ROI.
    • Tools/products: Federated data infrastructure; privacy-preserving analytics; annual benchmark reports.
    • Assumptions/Dependencies: Sustained funding; data-sharing agreements; ethics approvals.
  • Intersectionally refined measurement and sampling
    • What: Oversample Black and Indigenous physicists and disabled/gender-diverse respondents to enable disaggregated inference; expand beyond BIPOC aggregation.
    • Why: The study’s small N masks distinct patterns (e.g., elevated harm for Black and Indigenous physicists).
    • Tools/workflows: Stratified sampling frames; community partnerships; respondent protections for small cells.
    • Assumptions/Dependencies: Community trust; incentives; statistical disclosure control.
  • Standards and accreditation: “Safe Physics” certification for departments and conferences
    • What: Independent certification tied to meeting thresholds on harm disparities, reporting processes, accessibility, and enforcement.
    • Why: Creates market/reputational incentives for sustained climate improvement.
    • Tools/products: Compliance rubric; external audit teams; public registry.
    • Assumptions/Dependencies: Professional society governance; buy-in from major institutions.
  • Policy harmonization across funders and institutions
    • What: Align definitions, reporting taxonomies, and minimum response standards (mirroring those used in the study) across tri-council, universities, labs, and societies.
    • Why: Reduces confusion, improves comparability, increases accountability.
    • Tools/workflows: National policy working group; shared glossaries; legal reviews.
    • Assumptions/Dependencies: Inter-agency coordination; provincial/federal legal alignment.
  • Evaluation of accountability models (including restorative and sanction-based approaches)
    • What: Comparative trials of restorative justice, progressive discipline, and hybrid models for harassment cases in academic STEM.
    • Why: The paper underscores harm prevalence; evidence on “what works” in physics contexts is limited.
    • Tools/workflows: Outcome metrics (recidivism, satisfaction, climate change), randomized or quasi-experimental designs.
    • Assumptions/Dependencies: Ethical safeguards; participant consent; institutional risk tolerance.
  • Supervisor capability-building at scale
    • What: National curriculum and certification for PI/supervisor competencies (power dynamics, feedback without intimidation, trauma-informed practices).
    • Why: Supervisory power was implicated in qualitative reports; mid-career faculty show high harassment exposure.
    • Tools/products: Modular micro-credentials; practice simulations; re-certification cycles.
    • Assumptions/Dependencies: Faculty incentives; workload models; platform partners.
  • Accessibility-by-design for physics education and research
    • What: Redesign labs, instruments, computing workflows, and conferences using universal design principles; evaluate impact on disabled physicists’ harm exposure and retention.
    • Why: Disabled respondents are disproportionately harmed; structural fixes complement policy.
    • Tools/products: Accessible lab standards; vendor guidelines; conference RFP requirements.
    • Assumptions/Dependencies: Capital budgets; vendor ecosystem; building codes.
  • Early-warning “climate risk index” using awareness-of-harm signals
    • What: Model links between “awareness of harm” levels and subsequent reported incidents/turnover to create risk heatmaps by unit and career stage.
    • Why: The study establishes awareness as a climate indicator; predictive use can prioritize interventions.
    • Tools/workflows: Privacy-preserving analytics; thresholds and playbooks for escalation.
    • Assumptions/Dependencies: Sufficient time-series data; safeguards against misuse.
  • Cross-sector replication and benchmarking (industry, healthcare, government labs)
    • What: Extend the survey to private-sector R&D, hospitals (medical physics), and national labs; benchmark against academia to identify sector-specific risks.
    • Why: Physics-trained workers move across sectors; portability amplifies impact.
    • Tools/workflows: Sector-adapted instruments; comparative dashboards.
    • Assumptions/Dependencies: Employer cooperation; confidentiality protections.
  • Economic and workforce modeling of attrition costs
    • What: Quantify productivity loss, delayed degrees, replacement costs, and grant risk associated with harassment; model ROI of interventions.
    • Why: Strengthens the business case for sustained investment in EDI and safety.
    • Tools/workflows: Cost-of-attrition models; scenario analysis for funders and deans.
    • Assumptions/Dependencies: Access to HR and student progression data; credible counterfactuals.
  • Technology-enabled confidential support at scale
    • What: Secure, ethics-reviewed platforms for documentation, time-stamped evidence vaults, and guided reporting that preserve survivor control; analytics limited to aggregate climate.
    • Why: Many respondents experience harm but underreport; tech can lower barriers while protecting privacy.
    • Tools/products: End-to-end encrypted apps; consent-based data sharing; survivor resource hubs.
    • Assumptions/Dependencies: Security audits; legal compliance; clear non-surveillance boundaries.
  • Curriculum integration: Ethics, power, and inclusion in physics training
    • What: Embed short, assessed modules on harassment, bias, and inclusive teamwork across undergraduate and graduate physics curricula.
    • Why: Culture change is durable when norms are taught and practiced early.
    • Tools/workflows: Case-based learning, discipline-specific scenarios, competency assessments.
    • Assumptions/Dependencies: Curriculum governance; faculty development.
  • Coalition-based mentorship and sponsorship programs for Black, Indigenous, disabled, and gender-diverse physicists
    • What: Structured, cross-institution mentorship with sponsorship commitments (e.g., grant teams, invited talks), evaluated for harm reduction and career outcomes.
    • Why: The study’s descriptive patterns show these groups face elevated harm and barriers.
    • Tools/workflows: Mentor training, matching platforms, outcome tracking.
    • Assumptions/Dependencies: Mentor supply; sustained funding; evaluation design.
  • Legal and labor frameworks: Collective agreement clauses and third-party investigations
    • What: Negotiate explicit harassment clauses with timelines, protections against retaliation, and default to independent investigators for power-imbalanced cases.
    • Why: Qualitative data highlight supervisor power concerns; procedural justice underpins reporting.
    • Tools/workflows: Model contract language; roster of vetted investigators.
    • Assumptions/Dependencies: Union/employer negotiations; legal review.

Notes on feasibility across all applications:

  • The survey’s aggregation of racial categories and small-N for some groups mean local replications should prioritize oversampling and privacy protections.
  • Success depends on leadership commitment, trauma-informed practices, and transparent follow-through.
  • Data use must be governed to prevent surveillance or retaliation; emphasize aggregate climate improvement and survivor agency.
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Glossary

  • 2SLGBTQIA+: An acronym for Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, Intersex, Asexual, plus other sexual and gender minority identities. "including those who are women, 2SLGBTQIA+, Black, Indigenous, or Persons of Colour [BIPOC], or disabled"
  • ableism: Discrimination, prejudice, or systemic barriers against disabled people. "attitudes and interpersonal interactions are examples of ableism that excludes disabled physicists"
  • awareness of harm: In this study, knowing about others’ harmful experiences via witnessing or disclosures. "We refer to witnessing harm, hearing a disclosure of harm from the involved persons, or hearing about harm another way (e.g., through a third-party) as 'awareness of harm.'"
  • BIPOC: Acronym for Black, Indigenous, and People of Colour. "BIPOC communities may experience shared underrepresentation and discrimination"
  • Bonferroni corrections: A multiple-comparison adjustment that controls the family-wise error rate by tightening significance thresholds. "Kruskal- Wallis (KW) tests with Dunn post-hoc comparisons and Bonferroni corrections were applied."
  • Chi-square distribution: A probability distribution used to evaluate chi-square statistics in hypothesis tests. "The test produces an H-statistic, which is compared to the Chi-square distribution to assess statistical significance."
  • Chi-square tests of independence: Statistical tests assessing whether two categorical variables are associated. "we conducted four Chi- square tests of independence"
  • cisgender: Describing a person whose gender identity aligns with their sex assigned at birth. "gender diverse people report more harm in daily life than cisgender people on average"
  • climate-relevant indicator: A measure reflecting the cultural or social environment of an organization or setting. "We conceptualized awareness of harm as a climate-relevant indicator that captures informal knowledge networks within institutions"
  • descriptive statistics: Numerical summaries (e.g., means, percentages, standard deviations) that describe data. "we present both percentages and descriptive statistics."
  • disaggregated data: Data broken down into specific subgroups rather than combined into broad categories. "fully disaggregated racial data would ultimately provide more meaningful insights."
  • double-bind: A no-win situation created by conflicting expectations that constrain a person’s options. "creating a unique and fraught double-bind."
  • Dunn post-hoc comparisons: Pairwise non-parametric comparisons following a Kruskal–Wallis test. "Kruskal- Wallis (KW) tests with Dunn post-hoc comparisons and Bonferroni corrections were applied."
  • effect sizes: Quantitative measures of the magnitude or practical importance of an effect. "effect sizes were small"
  • G*Power: Software for computing statistical power and required sample sizes. "Power analyses using G*Power software indicated that 1721 participants were required to detect small effects."
  • gender diverse: Umbrella term for identities outside the man/woman binary (e.g., non-binary, genderqueer, agender). "we employed the term 'gender diverse' to describe those with gender identities distinct from 'man' or 'woman'"
  • H-statistic: The test statistic produced by the Kruskal–Wallis test. "The test produces an H-statistic, which is compared to the Chi-square distribution to assess statistical significance."
  • hyper invisibility: Being overlooked or excluded due to intersecting marginalized identities. "faced both hyper invisibility (being ignored or excluded)"
  • hyper visibility: Being excessively scrutinized or tokenized due to intersecting identities. "and hyper visibility (being scrutinized or tokenized)"
  • imposter feelings: Feelings of inadequacy or fraudulence despite evidence of competence. "higher levels of imposter feelings and reduced sense of belonging among undergraduate women in physics"
  • intersectional invisibility theory: Theory positing that people with intersecting marginalized identities are less recognized as prototypical and thus overlooked. "intersectional invisibility theory (Purdie-Vaughns & Eibach, 2008)"
  • intersectionality theory: Framework examining how overlapping social identities shape unique experiences of oppression and privilege. "exemplifying intersectionality theory"
  • Kruskal–Wallis test: A non-parametric test comparing distributions across three or more independent groups. "Kruskal- Wallis (KW) tests"
  • Mann–Whitney U test: A non-parametric test comparing two independent groups. "Mann-Whitney U tests."
  • meta-analysis: A statistical synthesis that aggregates results across multiple studies. "a recent meta-analysis (Nielsen et al., 2024)"
  • microaggressions: Subtle, often unintentional slights or insults directed at marginalized groups. "microaggressions, high teaching and service loads, and bias in promotion and tenure criteria"
  • non-parametric statistical test: Methods that do not assume a specific population distribution. "The KW test is a non-parametric statistical test used to determine whether there are statistically significant differences between the distributions of three or more independent groups."
  • omnibus test: An overall statistical test that evaluates whether any group differences exist before post-hoc comparisons. "the omnibus test did not reach statistical significance (p = . 10)"
  • power analyses: Calculations used to determine the sample size needed to detect an effect with desired power. "Power analyses using G*Power software indicated that 1721 participants were required to detect small effects."
  • qualitative meta-synthesis: An integrative method that synthesizes findings from multiple qualitative studies. "One qualitative meta-synthesis covering 42 studies found that BIPOC women in academia faced marked barriers"
  • somatic problems: Physical symptoms that can be associated with psychological stressors. "witnessing workplace bullying was associated with mental health and somatic problems"
  • statistical significance: An assessment that an observed effect is unlikely under the null hypothesis at a chosen alpha level. "the omnibus test did not reach statistical significance (p = . 10)"
  • tokenism: Superficial inclusion of underrepresented people without substantive change or support. "exposure to tokenism"
  • violations of statistical assumptions: Situations where data do not meet the prerequisites of a statistical test. "Due to violations of statistical assumptions and unequal sample sizes"
  • whisper networks: Informal channels through which people share warnings and information about misconduct. "sometimes referred to as “whisper networks" (Johnson, 2023; Jung & Mendoza 2023)."
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