Effectiveness of home fire safety interventions. A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To assess the effectiveness of Home Fire Safety (HFS) interventions versus other interventions/no interventions/controls on HFS knowledge and behaviour at short-, intermediate- and long-term follow ups. DESIGN: Systematic review and meta-analysis of randomized controlled trials. DATA SOURCES: MEDLINE, EMBASE and PubMed databases were searched from January 1998 to July 2018, and studies retrieved. PARTICIPANTS: Toddlers, children (primary or secondary school), teenagers or adults. INTERVENTIONS/COMPARISON: HFS interventions compared to other interventions / no interventions / controls. OUTCOMES: HFS knowledge and behaviour. RESULTS: 10 studies were identified (8 RCTs and 2 prospective cohort). Two studies assessed the effects of HFS interventions vs no interventions on HFS knowledge at up to 4 months follow up in school children and demonstrated significant difference between groups (very low quality, 2 RCTs, 535 participants, SMD 0.38, 95% CI: 0.21 to 0.55, p < 0.001). One study examined the effects of different modes of HFS interventions (computer-based vs instructor-led) on HFS knowledge and behaviour immediately post-intervention in adults and displayed no significant difference between groups (HFS knowledge; very low quality, 1 RCT, 68 participants, SMD -0.02, 95% CI: -0.50 to 0.45, p = 0.92) and (HFS behaviour; very low quality, 1 RCT, 68 participants, SMD 0.06, 95% CI: -0.41 to 0.54, p = 0.79) respectively. CONCLUSION: The limited evidence supports the use of HFS interventions to improve HFS knowledge and behaviour in children, families with children and adults.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.039 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".