Maximising the use of evidence: exploring the intersection between population health intervention research and knowledge translation from a Canadian perspective
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
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.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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