Learning About and Using Research Evidence Among Public Health Practitioners
Bibliographic record
Abstract
INTRODUCTION: Funders and accreditation standards increasingly call on state and local public health agencies to use the best available science. Using research evidence is a key process in practicing evidence-based decision making (EBDM). This study explored preferences for and uses of research evidence, and examined correlates regarding frequency of use. METHODS: In 2014, eligible staff from 12 state health departments and their partnering agencies were invited to complete an online self-report questionnaire and achieved an 82% response rate (1,237/1,509). The cross-sectional data analyzed in 2015 were baseline to a study on enhancing EBDM capacity and supports. RESULTS: Webinars/workshops was the most frequently selected method to learn public health findings among those in state and local health departments, whereas academic journals was the top selection by those in universities and healthcare facilities (p<0.001). Several modifiable EBDM practices were associated with more frequent use of research evidence, including direct supervisor expectations for EBDM use and performance evaluation based partially on EBDM use (AOR=2.5, 95% CI=1.9, 3.2 and AOR=2.5, 95% CI=2.1, 2.9, respectively). Increased numbers of EBDM practices were associated with higher odds of frequent research evidence use. Participant characteristics associated with higher research evidence use and adjusted for were job role, education attainment, and gender. CONCLUSIONS: To translate research into public health practice, researchers can tailor evidence on intervention implementation and effectiveness and disease burden to accessible and preferred formats for public health workers and partners. Management practices to support evidence-based disease prevention can be instituted and fostered in public health and partnering agencies.
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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 | 0.076 | 0.222 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".