From pressure in the pipeline to accelerating ascension: a survey to understand professional experiences of and opportunities for Canadian women in the healthcare sector
Bibliographic record
Abstract
BACKGROUND: Much has been written about the state and persistent lack of progress regarding gender equity and the commonly referenced phenomenon of a 'leaking pipeline'. This framing focuses attention on the symptom of women leaving the workforce, rather than the well-documented contributing factors of hindered recognition, advancement, and financial opportunities. While attention shifts to identifying strategies and practices to address gender inequities, there is limited insight into the professional experiences of Canadian women, specifically in the female-dominated healthcare sector. METHODS: We conducted a survey of 420 women working across a range of roles within healthcare. Frequencies and descriptive statistics were calculated for each measure as appropriate. For each respondent, two composite Unconscious Bias (UCB) scores were created using a meaningful grouping approach. RESULTS: Our survey results highlight three key areas of focus to move from knowledge to action, including (1) identifying the resources, structural factors, and professional network elements that will enable a collective shift towards gender equity; (2) providing women with access to formal and informal opportunities to develop the strategic relational skills required for advancement; and (3) restructuring social environments to be more inclusive. Specifically, women identified that self-advocacy, confidence building, and negotiation skills were most important to support development and leadership advancement. CONCLUSIONS: These insights provide systems and organizations with practical actions they can take to support women in the health workforce amid a time of considerable workforce pressure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".