AAPM WGWMRSC Report 420: chapter climate check: Mixed methods analysis of survey responses
Bibliographic record
Abstract
INTRODUCTION: The American Association of Physicists in Medicine (AAPM) recently shared results and recommendations from its first Equity, Diversity, and Inclusion (EDI) Climate Survey, which was designed to assess the climate at the workplace, the AAPM organization, and the AAPM regional chapter level. This work further explores the status of EDI at the regional chapter level. METHODS: AAPM's EDI Survey was distributed to 5500 members and had a response rate of 25%. In the survey, three open-ended comment boxes were provided for feedback, including one for regional AAPM members. Sixty-four percent of respondents indicated they were part of a regional chapter, and 6% provided written responses to the regional chapter question. Responses were analyzed using a mixed methods approach with an exploratory sequential design. Two phases were conducted; the first relied on a Grounded Theory quantitative systemic approach, and the second applied qualitative analysis. Chapter member demographic data were collected to support findings. RESULTS: Survey respondents provided open comments and feedback on their regional chapter's climate. Data are summarized as five themes: positive experiences, negative experiences, challenges within chapters, diversity and inclusion, and changes observed. Experiences of regional chapters were rated positively by 75% of respondents. Respondents found their chapters were welcoming, and some noted their great chapter leadership. A number of incidents of sexual harassment, bullying, and discrimination incidences were also shared. Other respondents observed exclusion based on their gender, race, highest degree, and medical physics specialty. Chapter leadership data aligned with these claims, with most leaders to-date being white males, doctoral degree holders, and/or specializing in radiation therapy. CONCLUSION: AAPM chapters provide rewarding professional opportunities. This study has highlighted positive and negative experiences reported by its members. The major themes identified can guide chapter leaders to continue to cultivate welcoming communities for regional AAPM members.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".