A mixed methods evaluation of capturing and sharing practitioner experience for improving local tobacco control strategies
Bibliographic record
Abstract
OBJECTIVE: Practitioner experience is one type of evidence that is used in public health planning and action. Yet, methods for capturing and sharing experience are under-developed. We evaluated the reach, uptake and use of an example of capturing and sharing practitioner experience from tobacco control known as documentation of practice (DoP) reports. METHODS: The participatory, mixed methods approach included the following: a document review to capture data related to the extent and how DoP reports reached the target population; an online survey to assess awareness, use and perceptions about DoP reports; and semi-structured interviews to identify and explore examples of instrumental, conceptual and symbolic use of DoP reports. The samples for the survey and interviews included tobacco control practitioners from public health units in Ontario, Canada. RESULTS: Seventy-three individuals participated in the survey and 10 were interviewed. Awareness of at least one DoP report was high. The most common way of learning about DoP reports was email. DoP reports focused on policy issues had highest use; these reports were used in conceptual (helped raise awareness), instrumental (directly informed local policy development) and symbolic (confirmed a choice already made) ways. DoP reports may be improved with key messages, shorter development timelines, more relevant topic selection and dissemination to audiences beyond public health. CONCLUSION: DoP reports are useful to public health practitioners working in tobacco control within Ontario; refinements to development and dissemination processes will enhance use. Future studies and adaptations of DoP reports could help improve use of practitioner experience as one source of evidence informing public health practice.
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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 | 0.208 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".