The perceived organizational impact of the gender gap across a Canadian department of medicine and proposed strategies to combat it: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Despite the gender parity existing in medical schools for over three decades, women remain underrepresented in academic medical centers, particularly in senior ranks and in leadership roles. This has consequences for patient care, education, research, and workplace culture within healthcare organizations. This study was undertaken to explore the perspectives of faculty members at a single department of medicine on the impact of the existing gender gap on organizational effectiveness and workplace culture, and to identify systems-based strategies to mitigate the gap. METHODS: The study took place at a large university department of medicine in Toronto, Canada, with six affiliated hospitals. In this qualitative study, semi-structured individual interviews were conducted between May and September 2016 with full-time faculty members who held clinical and university-based appointments. Transcripts of the interviews were analyzed using thematic analysis. Three authors independently reviewed the transcripts to determine a preliminary list of codes and establish a coding framework. A modified audit consensus coding approach was applied; a single analyst reviewed all the transcripts and a second analyst audited 20% of the transcripts in each round of coding. Following each round, inter-rater reliability was determined, discrepancies were resolved through discussion, and modifications were made as needed to the coding framework. The analysis revealed faculty members' perceptions of the gender gap, potential contributing factors, organizational impacts, and possible solutions to bridge the gap. RESULTS: Of the 43 full-time faculty members who participated in the survey (29 of whom self-identified as female), most participants were aware of the existing gender gap within academic medicine. Participants described social exclusion, reinforced stereotypes, and unprofessional behaviors as consequences of the gap on organizational effectiveness and culture. They suggested improvements in (1) the processes for recruitment, hiring, and promotion; (2) inclusiveness of the work environment; (3) structures for mentorship; and (4) ongoing monitoring of the gap. CONCLUSION: The existing gender gap in academic medicine may have negative consequences for organizational effectiveness and workplace culture but many systems-based strategies to mitigate the gap exist. Although these solutions warrant rigorous evaluation, they are feasible to institute within most healthcare organizations immediately.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".