FACTORS OF THE INNOVATION, ORGANIZATION, ENVIRONMENT, AND INDIVIDUAL THAT PREDICT THE INFLUENCE FIVE SYSTEMATIC REVIEWS HAD ON PUBLIC HEALTH DECISIONS
Bibliographic record
Abstract
OBJECTIVE: To determine the extent to which systematic reviews of public health interventions influenced public health decisions and which factors were associated with influencing these decisions. METHODS: This cross-sectional follow-up survey evaluated the use of five systematic reviews in public health decision making. Independent variables included characteristics of the innovation, organization, environment, and individual. Primary data were collected using a telephone survey and a self-administered organizational demographics questionnaire. Public health decision makers in all 41 public health units in Ontario were invited to participate in the study. Multiple linear regression analyses on the five program decisions were conducted. RESULTS: The systematic reviews were perceived as having the greatest amount of influence on decisions related to program justification and program planning, and the least influence on program evaluation decisions. The greater the perception that one's organization valued the use of research evidence for decision making and that ongoing training in the critical appraisal of research literature was provided, the greater the perception of the influence the systematic review had on public health decisions. CONCLUSIONS: Organizational characteristics are important predictors of the use of systematic reviews in public health decision making. Future dissemination strategies need to promote the value of using systematic reviews for program decision making as well as promote ongoing training in critical appraisal among intended users in Ontario.
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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 | 0.078 | 0.323 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".