An interactive method for engaging the public health workforce with evidence
Bibliographic record
Abstract
Systematic review authors are increasingly directing their attention to not only ensuring the robust processes and methods of their syntheses, but also to facilitating the use of their reviews by public health decision-makers and practitioners. This latter activity is known by several terms including knowledge translation, for which one definition is a ‘dynamic and iterative process that includes synthesis, exchange and ethically sound application of knowledge’.1 Unfortunately—and despite good intentions—the successful translation of knowledge has at times been inhibited by the failure of reviews to meet the needs of decision-makers, and the limitations of the traditional avenues by which reviews are disseminated.2 Encouraging the utilization of reviews by the public health workforce is a complex challenge. An unsupportive culture within the workforce, a lack of experience in assessing evidence, the use of traditional academic language in communication and the lack of actionable messages can all act as barriers to successful knowledge translation.3 Improving communication through developing strategies that include summaries, podcasts, webinars and translational tools which target key decision-makers such as HealthEvidence.org should be considered by authors as promising actions to support the uptake of reviews into practice.4,5 Earlier work has also suggested that to better meet the research evidence needs of public health professionals, authors should aim to produce syntheses that are actionable, relevant and timely.2 Further, review authors must interact more with those who will, or could use their reviews; particularly when determining the scope and questions to which a review will be directed.2
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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 | 0.076 | 0.171 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.094 | 0.016 |
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".