ral ssBioMed CentBMC Health Services Research Open AcceStudy protocol Provincial prenatal record revision: a multiple case study of evidence-based decision-making at the population-policy level
Bibliographic record
Abstract
Background: There is a significant gap in the knowledge translation literature related to how research evidence actually contributes to health care decision-making. Decisions around what care to provide at the population (rather than individual) level are particularly complex, involving considerations such as feasibility, cost, and population needs in addition to scientific evidence. One example of decision-making at this "population-policy " level involves what screening questions and intervention guides to include on standardized provincial prenatal records. As mandatory medical reporting forms, prenatal records are potentially powerful vehicles for promoting population-wide evidence-based care. However, the extent to which Canadian prenatal records reflect best-practice recommendations for the assessment of well-known risk factors such as maternal smoking and alcohol consumption varies markedly across Canadian provinces and territories. The goal of this study is to better understand the interaction of contextual factors and research evidence on decision-making at the population-policy level, by examining the processes by which provincial prenatal records are reviewed and revised.
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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 | 0.098 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.078 | 0.008 |
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".