Local Health Department Accreditation Is Associated With Organizational Supports for Evidence-Based Decision Making
Bibliographic record
Abstract
Introduction: Recent studies show health department accreditation from the U.S. Public Health Accreditation Board (PHAB) drives performance management and quality improvement. Domain 10 of the PHAB standards calls for agencies to use evidence in decision-making. It is unknown whether accreditation is associated with organizational supports for evidence-based decision making (EBDM). Self-report data from a 2017 survey of U.S. local health departments were analyzed to test relationships of accreditation status with organizational supports for EBDM. Methods: A cross-sectional survey was conducted in this observational study. A total of 579 local health departments were invited to complete an online survey; 350 (60.4%) provided complete data for the present study. The dependent variables were six factors of organizational supports for EBDM previously validated through confirmatory factor analyses. Accreditation status (PHAB-accredited, preparing, not preparing) was the independent variable of interest. Logistic regression analyses controlled for governance (presence of a local board of health; state, local, or shared state and local governance) and jurisdiction population size. Results: PHAB-accredited health departments were more likely to report higher capacity for EBDM, resource availability for EBDM, and evaluation capacity than health departments that reported not yet preparing for accreditation. Health departments that reported preparing for PHAB accreditation showed a non-significant pattern of higher perceived supports for EBDM compared to departments not preparing for accreditation. Conclusion: PHAB standards may help stimulate health department organizational supports for EBDM.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".