Building capacity for evidence-informed decision making in Canadian public health
Bibliographic record
Abstract
Our research team partnered with three Canadian public health departments to study the impact of tailored knowledge translation and exchange (KTE) strategies on evidence-informed decision making (EIDM). We aimed to enhance public health EIDM knowledge, skills and behaviour and further facilitate organizational contexts conducive to EIDM. We used case study methodology and tailored the intervention and analysis to each unique case (i.e. health department). An experienced Knowledge Broker supported each case through a variety of strategies: large-group training with front-line staff; one-on-one consultation with specialists, guiding them through a structured EIDM process; and advice to management on organizational policies and procedures. Data were collected prior to, during, and following the intervention via an online survey (demographic information, self-reported EIDM behaviours, and social networks) and in-person assessment (EIDM knowledge and skills). Results across the three cases revealed a significant increase in EIDM knowledge and skills at follow-up, among those who worked closely with the Knowledge Broker (2.8 points out of a possible 36 points, (95% CI 2.0 to 3.6, p < 0.001)). Similarly, staff who worked closely with the Knowledge Broker showed significant improvement in the frequency of EIDM-related behaviours (OR 1.33, 95% CI 1.04 to 1.78, p = 0.02). Those not intensively involved, but who sought information from a peer considered an "expert", showed statistically significant improvements in EIDM behaviours (standardized beta coefficient: 0.29, p < 0.0001). Staff who were more central within the social network also showed greater improvement in EIDM behaviour at follow-up (standardized beta coefficient: 0.21, p= 0.09). These positive effects were sustained when organizational mechanisms were present. EIDM knowledge, skills and behaviours improved as a result of KTE strategies tailored to the unique needs of each health department. These findings suggest effective methods for developing capacity for EIDM and provide specific support for a tailored approach. This research was supported by the Canadian Institutes of Health Research (FRN 101867, 126353).
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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 | 0.067 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".