Comparing views about evidence in Ontario public health units: a qualitative descriptive study
Bibliographic record
Abstract
Background: ways of perceiving evidence by public health managers, practitioners and policymakers is one of the key determinants of evidence uptake. Recent public health policy in Ontario requires programmes to be based on evidence. Therefore, understanding views about evidence in both practice and policy contexts is important to bridge the research-policy-practice gap in public health. Objective and methods: this qualitative descriptive study examined understandings about evidence in Ontario public health units by comparing perspectives from managers and frontline staff across six geographically-diverse units. A secondary qualitative content analysis was used to re-analyse transcripts of focus groups from the Renewal of Public Health Systems (RePHS) research project. Results: similarities and differences were revealed with respect to how public health managers and frontline staff view evidence. Although both managers and frontline staff understand that multiple forms of evidence exist and that these forms must be integrated when making decisions regarding programme development and implementation, frontline staff highlighted the role of practice-based evidence. Both groups named tools and processes that were available to assist their decision making. Frontline staff indicated capacity building as important for supporting evidence use. Both groups noted that leadership could present a challenge to evidence-based programmes if not supportive of the evidence-based solution for public health problems. However, the understanding of leadership differed between frontline staff and managers. Conclusion: findings from this study provide insight into how use of evidence can be promoted and how to better support policy implementation efforts within practice contexts.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".