FACTORS AFFECTING THE UTILIZATION OF SYSTEMATIC REVIEWS
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which public health decision makers used five systematic reviews to make policy decisions, and to determine which characteristics predict their use. METHODS: This cross-sectional follow-up study of public health decision makers in Ontario collected primary data using a telephone survey and a short, self-administered organizational demographics questionnaire completed by the administrative assistant for each Medical Officer of Health. Independent variables included characteristics of the innovation, organization, environment, and individual. Data were entered into a computerized database developed specifically for this study, and multiple logistic regression analysis was conducted. RESULTS: The participation rate was very high, with 85% of public health units and 96% of available decision makers completing the survey. In addition, 63% of respondents stated they had used at least one of the systematic reviews in the previous 2 years to make a decision. The most important predictors of use were one's position, expecting to use a review in the future, and perceptions that the reviews were easy to use and that they overcame the barrier of limited critical appraisal skills. CONCLUSIONS: Utilization of the systematic reviews in Ontario was very high. The utilization rates found in this study were significantly higher than those reported in previous utilization studies. One's position was found to be the strongest predictor of use, identifying program managers and directors as the most appropriate audience for systematic reviews.
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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 | 0.108 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".