Injury data in British Columbia: policy maker perspectives on knowledge transfer
Bibliographic record
Abstract
Provincial and regional decision makers in the injury prevention field were interviewed in British Columbia (B.C.) to obtain their views about best processes for the transfer or dissemination of relevant data. These decision makers (n = 13) indicated that data should provide them with a holistic and comprehensive picture to support their decision processes. In addition, they felt information about injury types and rates should be linked backward to determinants or causes and forward to consequences or outcomes. This complete chain of data is needed for planning and evaluating health promotion interventions. It was also felt that data providers needed to devote more effort to fostering effective receptor capacity, so that injury prevention professionals will be better able to understand, interpret and apply the data. These findings can likely be generalized to other jurisdictions and policy areas, and offer additional insight into the practicalities of knowledge transfer and exchange in researcher/decision maker partnerships.
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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 | 0.159 | 0.231 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.030 | 0.010 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| 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".