Maximising the use of evidence: exploring the intersection between population health intervention research and knowledge translation from a Canadian perspective
Bibliographic record
Abstract
Population and public health research has been shifting from describing factors that shape health to an interrogation of the processes and outcomes underpinning policy and programme interventions. This shift has given rise to acknowledging population health intervention research (PHIR) as a distinct field of study in Canada. Given that PHIR aims to maximise the use of evidence to inform interventions, a discussion paper was written and a workshop was held, with 24 participants working across policy, practice and research, to identify distinct features of PHIR that create opportunities and challenges for knowledge translation (KT). Building on the discussion paper and activities at the workshop, workshop participants surfaced five features of PHIR that need specific consideration to facilitate progress on understanding and capitalising on the relationships between KT and PHIR. Implications for stakeholders interested in maximising the use of evidence to inform strategies for chronic disease prevention are also provided.
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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 | 0.358 | 0.354 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.022 | 0.030 |
| Science and technology studies | 0.035 | 0.071 |
| Scholarly communication | 0.062 | 0.023 |
| Open science | 0.010 | 0.039 |
| Research integrity | 0.011 | 0.016 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".