Information-Seeking Behaviors and Other Factors Contributing to Successful Implementation of Evidence-Based Practices in Local Health Departments
Bibliographic record
Abstract
In Brief The objective of this article was to describe factors that contribute to successful translation of science into evidence-based practices and their implementation in public health practice agencies, based on a review of the literature and evidence from a series of case studies. The case studies involved structured interviews with key informants in 4 health departments and with 4 corresponding partners from academic institutions. Interviews were recorded and transcribed, coded by 2 independent, trained coders, using a standard codebook. A thematic analysis of codes was conducted. Coding was entered into Atlas TI software for further analysis. Results from the literature review indicated that only approximately half of programs implemented in state and local health departments were evidence based. Lack of time, inadequate funding, and absence of cultural and managerial support—including incentives—are among the most commonly cited barriers to implementing evidence-based practices. Findings from the case studies suggest that these health departments, successful in implementing evidence-based practices, have strong relationships and good communication channels established with their academic partner(s). There is strong leadership engagement from within the health department and in the academic institution. Implementation of evidence-based programs was most often related to high priority community needs and the availability of resources to address these needs. The practice agencies operate with a culture of quality improvement throughout the agency. Information technology, training, how the interventions are bundled, including their complexity and ability to be customized and resource requirements are all fruitful avenues for further research. This article describes factors that contribute to successful implementation of public health science. Health departments that are successful in implementing evidence-based practices have strong relationships and good communication channels established with their academic partner(s). Implementation of evidence-based programs was most often related to high priority community needs and the availability of resources to address these needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".