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Record W1951751055 · doi:10.1186/s12889-015-2171-x

A systematic review and meta-analysis of the effectiveness of food safety education interventions for consumers in developed countries

2015· review· en· W1951751055 on OpenAlexafffund
Ian Young, Lisa Waddell, Shannon Harding, Judy Greig, Mariola Mascarenhas, Bhairavi Sivaramalingam, Mai Pham, Andrew Papadopoulos

Bibliographic record

VenueBMC Public Health · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of GuelphPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicinePsychological interventionMeta-analysisBiostatisticsRandomized controlled trialSystematic reviewBlindingData extractionEnvironmental healthPublic healthMEDLINEPopulationFamily medicineNursingSurgeryPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Foodborne illness has a large public health and economic burden worldwide, and many cases are associated with food handled and prepared at home. Educational interventions are necessary to improve consumer food safety practices and reduce the associated burden of foodborne illness. METHODS: We conducted a systematic review and targeted meta-analyses to investigate the effectiveness of food safety education interventions for consumers. Relevant articles were identified through a preliminary scoping review that included: a comprehensive search in 10 bibliographic databases with verification; relevance screening of abstracts; and extraction of article characteristics. Experimental studies conducted in developed countries were prioritized for risk-of-bias assessment and data extraction. Meta-analysis was conducted on data subgroups stratified by key study design-intervention-population-outcome categories and subgroups were assessed for their quality of evidence. Meta-regression was conducted where appropriate to identify possible sources of between-trial heterogeneity. RESULTS: We identified 79 relevant studies: 17 randomized controlled trials (RCTs); 12 non-randomized controlled trials (NRTs); and 50 uncontrolled before-and-after studies. Several studies did not provide sufficient details on key design features (e.g. blinding), with some high risk-of-bias ratings due to incomplete outcome data and selective reporting. We identified a moderate to high confidence in results from two large RCTs investigating community- and school-based educational training interventions on behaviour outcomes in children and youth (median standardized mean difference [SMD] = 0.20, range: 0.05, 0.35); in two small RCTs evaluating video and written instructional messaging on behavioural intentions in adults (SMD = 0.36, 95% confidence interval [CI]: 0.02, 0.69); and in two NRT studies for university-based education on attitudes of students and staff (SMD = 0.26, 95% CI: 0.10, 0.43). Uncontrolled before-and-after study outcomes were very heterogeneous and we have little confidence that the meta-analysis results reflect the true effect. Some variation in outcomes was explained in meta-regression models, including a dose effect for behaviour outcomes in RCTs. CONCLUSIONS: In controlled trials, food safety education interventions showed significant effects in some contexts; however, many outcomes were very heterogeneous and do not provide a strong quality of evidence to support decision-making. Future research in this area is needed using more robust experimental designs to build on interventions shown to be effective in uncontrolled before-and-after studies.

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)0.042
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.103
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0260.049
Bibliometrics0.0140.011
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.279
GPT teacher head0.414
Teacher spread0.135 · 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 designMeta-analysis
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

Citations78
Published2015
Admission routes2
Has abstractyes

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