“It’s a Feel. That’s What a Lot of Our Evidence Would Consist of ”: Public Health Practitioners’ Perspectives on Evidence
Bibliographic record
Abstract
This article describes how evidence is defined and used in two British Columbia public health departments during the implementation of a Healthy Living initiative in 2009. Through interviews with 21 public health staff and decision makers, the author sought to investigate how "evidence" was defined by both frontline and management staff and how it was used in decision making. The authors found public health staff, particularly frontline practitioners, to be drawn to grassroots and local "lived experience" evidence. This tacit wisdom, in combination with evidence from academia and clinical evidence accessed through disciplinary or professional networks, offered a knowledge transition opportunity to inform decision making, rather than what can be characterized in the literature as unidirectional knowledge translation. It is often difficult for staff to digest and interpret research as part of their work day because of the volume and density of information that typically counts as evidence. Moreover, there exist challenges to identify and gather indicators as evidence of their work.
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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 | 0.295 | 0.332 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.017 | 0.089 |
| Scholarly communication | 0.039 | 0.033 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.022 | 0.045 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".