From the sticky floor to the glass ceiling and everything in between: protocol for a systematic review of barriers and facilitators to clinical academic careers and interventions to address these, with a focus on gender inequality
Bibliographic record
Abstract
Abstract Background Gender inequality within academic medicine and dentistry is a well-recognised issue, but one which is not completely understood in terms of its causes, or interventions to facilitate equality. This systematic review aims to identify, critically appraise, and synthesise the literature on facilitators and barriers to progression through a clinical academic career across medicine and dentistry. It will also explore interventions developed to increase recruitment and retention to clinical academic careers, with a particular focus on gender inequality. Methods The search will cover five databases (MEDLINE (including MEDLINE Epub Ahead of Print, MEDLINE In-Process & Other Non-Indexed Citations, and MEDLINE Daily), Cochrane Controlled Register of Trials (CENTRAL), PsycINFO, and Education Resource Information Center (ERIC)), reference lists, and forward citation searching. We will include studies of doctors, dentists, and/or those with a supervisory role over their careers, with or without an academic career. Outcomes will be study defined, but relate to success rates of joining or continuing within a clinical academic career, including but not limited to success in gaining funding support, proportion of time spent in academic work, and numbers of awards/higher education qualifications, as well as experiences of professionals within the clinical academic pathway. Study quality will be assessed using the Cochrane risk of bias tool for randomised controlled trials, the Newcastle-Ottawa tool for non-randomised studies, and the QARI tool for qualitative studies. Detailed plans for screening, data extraction, and analysis are provided within this protocol. Discussion This systematic review is situated within a larger project evaluating gender inequalities in clinical academic careers. This review will identify and synthetize barriers, facilitators, and interventions addressing gender inequalities in clinical academia. Our findings will increase awareness of inequalities in clinical academic careers through informing clinical academics, regulators and funders of the issues involved, and potential interventions to counteract these. Results will be published in a peer-reviewed journal. Systematic review registration Open Science Framework: https://osf.io/mfy7a
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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 | 0.136 | 0.124 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.027 | 0.022 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".