Explaining Consumer Safe Food Handling Through Behavior-Change Theories: A Systematic Review
Bibliographic record
Abstract
Consumers often engage in unsafe food handling behaviors at home. Previous studies have investigated the ability of behavior-change theories to explain and predict these behaviors. The purpose of this review was to determine which theories are most consistently associated with consumers' safe food handling behaviors across the published literature. A standardized systematic review methodology was used, consisting of the following steps: comprehensive search strategy; relevance screening of identified references; confirmation of relevance and characterization of relevant articles; risk-of-bias assessment; data extraction; and descriptive analysis of study results. A total of 20 relevant studies were identified; they were mostly conducted in Australia (40%) and the United States (35%) and used a cross-sectional design (65%). Most studies targeted young adults (65%), and none focused on high-risk consumer groups. The outcomes of 70% of studies received high overall risk-of-bias ratings, largely due to a lack of control for confounding variables. The most commonly applied theory was the Theory of Planned Behavior (45% of studies), which, along with other investigated theories of behavior change, was frequently associated with consumer safe food handling behavioral intentions and behaviors. However, overall, there was wide variation in the specific constructs found to be significantly associated and in the percentage of variance explained in each outcome across studies. The results suggest that multiple theories of behavior change can help to explain consumer safe food handling behaviors and could be adopted to guide the development of future behavior-change interventions. In these contexts, theories should be appropriately selected and adapted to meet the needs of the specific target population and context of interest.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".