Effectiveness of online learning on health researcher capacity to appropriately integrate sex, gender, or both in grant proposals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To describe the effectiveness of online learning to augment academic capacity to consider sex and gender in the conduct of basic science, clinical research, and population health studies. METHOD: The analysis compares pre- and post-test scores from 1441 individuals who completed the Canadian Institutes of Health Research Institute of Gender and Health's interactive e-learning modules between February 2016 and May 2017. The tests measured knowledge, self-efficacy, and self-reported intent to change behavior for three competencies: (1) the ability to appropriately define and distinguish between sex-related versus gender-related variables, (2) the application of methods for integrating sex and gender, and (3) the critical appraisal of sex and gender integration in the design, methods, and analysis plan of research proposals and publications. RESULTS: Of the 543 individuals who completed the basic science module, 62% demonstrated improved knowledge, and 86% increased self-efficacy across all competencies. Gains in knowledge and self-efficacy also occurred among 84% and 77% of completers of the human data collection module (n = 463) and among 73% and 82% of those who completed the secondary data analysis module (n = 435). In aggregate, 95% of participants reported an intent to change their behavior with respect to sex and gender in health research. CONCLUSIONS: Interactive online learning combined with feedback and self-assessment results in improved knowledge and self-efficacy for integrating sex and gender in health research.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it