Systematic review of blue-light service collaboration for community health and well-being
Bibliographic record
Abstract
Effective cross-service collaboration has been posed as a way of improving outcomes for people, enhancing community safety and well-being, reducing social and health inequalities, and improving service resource efficiencies. However, it was not known what evidence and frameworks existed for service leads to reform collaborative public service responses. This systematic review aimed to summarize evidence to understand best ways for police, fire, and ambulance services to collaborate to improve community safety and well-being. Standard methodology was used following PRISMA guidance. The search strategy optimized report retrieval from a broad range of academic databases, grey literature, and citation handsearching from January 2012 to March 2022. Endnote 8 supported data management. Eligible reports explored collaboration benefits between any two emergency services to improve any aspect of community safety or well-being and had to provide relevant extractable information. Critical appraisal and syntheses of findings were conducted. Studies could originate from any country. Records were screened and retrieved by one author and included reports independently double-screened. From the academic databases, 4,648 reports were identified and screened, of which 25 reports were retrieved and assessed for eligibility, but no relevant studies were retained following full text review. A further 27 records were identified from websites and citation searching, of which three were included following eligibility checks. The scant evidence uncovered in this review tentatively suggests service collaboration initiatives have potential for decreased resource use, increased public confidence, faster responses, increased survival rates, and reduced unnecessary emergency responses. Robust evidence is needed to influence policy and 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 | 0.030 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".