Public Health Decision‐Makers' Informational Needs and Preferences for Receiving Research Evidence
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to identify decision-makers' preferences for the transfer and exchange of research knowledge. This article is focused on how the participants define evidence-based decision-making and their preferences for receiving research evidence to integrate into the decision-making process. METHODS: Semistructured interviews were conducted with a purposive sample of 16 Ontario public health decision-makers from six Ontario public health units in this fundamental qualitative descriptive study. The sample included nine program managers, six directors, and one Medical Officer of Health. Participants were asked to define the term evidence-based decision-making and identify preferred research dissemination strategies. The interviews were audio-taped, transcribed verbatim, and coded for emerging concepts. RESULTS: Participants defined evidence-based decision-making as a process whereby multiple sources of information were consulted before making a decision concerning the provision of services. To facilitate integration of research evidence into the decision-making process, public health administrators appreciate receiving, in both electronic and hard copy, systematic reviews, executive summaries of research, and clear statements of implications for practice from health service researchers. CONCLUSIONS: Although consensus exists among participants concerning the definition of evidence based public health decision-making, ongoing efforts are required to continue to promote the use of research evidence in program planning and public health policy. It is also important to continue to improve the ease with which public health decision-makers access systematic reviews, as well as to ensure the relevance and applicability of the results to the practice setting.
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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 | 0.120 | 0.210 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".