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
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 members' 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.069 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".