Understanding evidence: a statewide survey to explore evidence-informed public health decision-making in a local government setting
Bibliographic record
Abstract
BACKGROUND: The value placed on types of evidence within decision-making contexts is highly dependent on individuals, the organizations in which the work and the systems and sectors they operate in. Decision-making processes too are highly contextual. Understanding the values placed on evidence and processes guiding decision-making is crucial to designing strategies to support evidence-informed decision-making (EIDM). This paper describes how evidence is used to inform local government (LG) public health decisions. METHODS: The study used mixed methods including a cross-sectional survey and interviews. The Evidence-Informed Decision-Making Tool (EvIDenT) survey was designed to assess three key domains likely to impact on EIDM: access, confidence, and organizational culture. Other elements included the usefulness and influence of sources of evidence (people/groups and resources), skills and barriers, and facilitators to EIDM. Forty-five LGs from Victoria, Australia agreed to participate in the survey and up to four people from each organization were invited to complete the survey (n = 175). To further explore definitions of evidence and generate experiential data on EIDM practice, key informant interviews were conducted with a range of LG employees working in areas relevant to public health. RESULTS: In total, 135 responses were received (75% response rate) and 13 interviews were conducted. Analysis revealed varying levels of access, confidence and organizational culture to support EIDM. Significant relationships were found between domains: confidence, culture and access to research evidence. Some forms of evidence (e.g. community views) appeared to be used more commonly and at the expense of others (e.g. research evidence). Overall, a mixture of evidence (but more internal than external evidence) was influential in public health decision-making in councils. By comparison, a mixture of evidence (but more external than internal evidence) was deemed to be useful in public health decision-making. CONCLUSIONS: This study makes an important contribution to understanding how evidence is used within the public health LG context. TRIAL REGISTRATION: ACTRN12609000953235.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".