His opportunity, her burden: A narrative critical review of why women decline academic opportunities
Bibliographic record
Abstract
OBJECTIVES: This paper stems from a desire to deepen our own understanding of why women might 'say no' when allies and sponsors offer or create opportunities for advancement, leadership or recognition. The resulting disparity between representation by men and women in leadership positions, invited keynote speakers and publication counts in academic medicine is a stubborn and wicked problem that requires a synthesis of knowledge across multidisciplinary literature. Acknowledging the complexity of this topic, we selected a narrative critical review methodology to explore reasons why one man's opportunity might be a woman's burden in academic medicine. METHODS: We engaged with an iterative process of identifying, reviewing and interpreting literature from Psychology (cognitive, industrial and educational), Sociology, Health Professions Education and Business, placing no restrictions on context or year of publication. Knowledge synthesis and interpretation were guided by our combined expertise, lived experience, consultations with experts outside the author team and these guiding questions: (1) Why might women have less time for career advancement opportunities? (2) Why do women have less time for research and leadership? (3) How are these disparities maintained? RESULTS: Turning down an opportunity may be a symptom of a much larger issue. The power of social expectations, culture and gender stereotypes remains a resistant force against calls for action. Consequently, women disproportionately take on other tasks that are not as well recognised. This disparity is maintained through social consequences for breaking with firmly entrenched stereotypes. CONCLUSIONS: Popular strategies like 'lean into opportunities', 'fake it till you make it' and 'overcome your imposter syndrome' suggest that women are standing in their own way. Critically, these axioms ignore powerful systemic barriers that shape these choices and opportunities. We offer strategies that allies, sponsors and peers can implement to offset the power of stereotypes.
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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 | 0.048 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".