Reflections on effective services: the art of evidence-based programming
Bibliographic record
Abstract
Third sector organisations (TSOs) are playing an increasingly prominent role in delivering health and social care services to the public. It is therefore important to ensure that such services are safe, equitable and effective. One way to achieve this is by incorporating evidence-based programmes (EBPs) and research into practice. Drawing on the broad literature, this article examines the values and knowledge preferences of TSOs and how these influence the incorporation of EBPs and related activities. Also discussed are the various factors that have an impact on successful EBP adoption and evidence use in the third sector and ways to maximise TSOs’ knowledge use. Informed by the perspective of a community-based mental health worker and academic researchers who engage in knowledge translation with TSOs, this discussion provides implications for practice and future research. Two recommendations are proposed: greater understanding of the knowledge-to-action pipeline; and the exploration and study of collaborations between TSOs and researchers.
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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 | 0.486 | 0.551 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.008 | 0.098 |
| Scholarly communication | 0.042 | 0.065 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.029 | 0.061 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".