How integrated knowledge translation worked to reduce federal policy barriers to the implementation of medication abortion in Canada: a realist evaluation
Bibliographic record
Abstract
Abstract Background Initial Canadian federal regulations for the abortion pill, mifepristone, had the potential to impede safe and equitable access to this medication. To catalyze evidence-based regulatory change, we engaged health policy, health system, and health services decision makers, and health professional organizations in integrated knowledge translation (iKT), a research approach that engages the users of research as equal partners. Methods We conducted a realist evaluation of what iKT strategies worked, for whom, and in what context to impact federal mifepristone regulations. We constructed initial program theories (if–then statements about how iKT worked). We tested the initial program theories using interviews with researchers and knowledge partners and triangulated with analysis of research programme documents. We configured the evidence in relation to the initial program theories, and refined program theories into causal explanatory configurations. Results We analyzed 38 interviews with researchers, health professional leaders, advocacy group leaders, and administrative government policy makers, as well as 49 program documents. Our results indicated that researcher partnerships with stakeholders had a meaningful impact on the removal of restrictions. We found key components of the causal explanatory configurations included: researcher motivation to move evidence into action, trusted reputations as credible sources of evidence, strategic partnerships, understanding of health policy processes, and researcher roles as a trusted convenor between key groups and decision makers. Conclusions Our study identifies several practical and transferable approaches to impactful iKT. The findings may be of relevance to researchers focused on public health topics subject to stigma.
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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 | 0.149 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".