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The 'WHO Safe Communities' model for the prevention of injury in whole populations

2005· review· en· W4252399350 on OpenAlexaboutno aff
Anneliese Spinks, Jim Nixon, Rod McClure

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2005
Typereview
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDemographyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: The safe communities approach has been embraced around the world as a model for coordinating community efforts to enhance safety and reduce injury. Over 80 communities throughout the world have been formally designated as 'Safe Communities' by the World Health Organization. It is of public health interest to determine to what degree the model is successful, and whether its application does indeed reduce injury rates in communities to which it is introduced. OBJECTIVES: To determine the effectiveness of the Safe Communities model to prevent injury in whole populations, or targeted sub-groups of populations. SEARCH STRATEGY: The search strategy was based on electronic searches, handsearches of selected journals, snowballing from reference lists of selected publications and contacting a key person from each WHO-designated Safe Community. SELECTION CRITERIA: Studies were independently screened for inclusion by two reviewers. Included studies were those conducted within a WHO Safe Community that reported changes in population injury rates within the community compared to a control community. DATA COLLECTION AND ANALYSIS: Data were independently extracted by two reviewers. Meta-analysis was not appropriate, due to the heterogeneity of the included studies. MAIN RESULTS: Only seven WHO Safe Communities, of more than 80 worldwide, have undertaken controlled evaluations using objective sources of injury data. These communities represent only four countries from two geographical regions in the world: the Scandinavian countries of Sweden and Norway and the Pacific nations of Australia and New Zealand. Safe Communities in Sweden and Norway have resulted in significant reductions in injury rates. The Australian and New Zealand communities have been unable to replicate the same level of success. AUTHORS' CONCLUSIONS: Evidence suggests the WHO Safe Communities model is effective in reducing injuries in whole populations. However, important methodological limitations exist in all studies from which evidence can be obtained. A lack of reported detail makes it unclear which factors facilitate or hinder a programme's success, and makes uncertain, whether the success of any particular application of the model is necessarily replicable in other communities. In evaluated programmes that did not report significant decreases in injury rates, this lack of information makes it difficult to distinguish between evidence of no effect of the model, or no evidence of effect. The four countries that have evaluated their Safe Communities with a sufficiently rigorous study design have higher economic wealth and health standards and lower injury rates than much of the world. No evaluations were available from other parts of the world, despite the designation of WHO Safe Communities in countries such as South Africa, Bangladesh, China, Vietnam, Canada, UK and USA. Generalisation of results of studies conducted in just four countries, to the international population needs to be done with caution. There is a need for more high-quality, methodologically strong evaluations of the model in a range of diverse communities and detailed reporting of implementation processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.085
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.085
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0850.098
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0080.014
Bibliometrics0.0130.009
Science and technology studies0.0010.003
Scholarly communication0.0050.008
Open science0.0060.004
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.287
GPT teacher head0.456
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations33
Published2005
Admission routes1
Has abstractyes

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