Incorporating sex, gender and vulnerable populations in a large multisite health research programme: The Ontario Pharmacy Evidence Network as a case study
Bibliographic record
Abstract
Abstract Background Funders now frequently require that sex and gender be considered in research programmes, but provide little guidance about how this can be accomplished, especially in large research programmes. The purpose of this study is to present and evaluate a model for promoting sex- and gender-based analysis (SGBA) in a large health service research programme, the Ontario Pharmacy Evidence Network (OPEN). Methods A mixed method study incorporating (1) team membersâ critical reflection, (2) surveys (n = 37) and interviews (n = 23) at programme midpoint, and (3) an end-of-study survey in 2016 with OPEN research project teams (n = 6). Results Incorporating gender and vulnerable populations (GVP) as a cross-cutting theme, with a dedicated team and resources to promote GVP research across the programme, was effective and well received. Team members felt their knowledge was improved, and the programme produced several sex- and gender-related research outputs. Not all resources were well used, however, and better communication of the purposes and roles of the team could increase effectiveness. Conclusions The experience of OPEN suggests that dedicating resources for sex and gender research can be effective in promoting SGBA research, but that research programmes should also focus on communicating the importance of SGBA to their members.
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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 | 0.055 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".