Intentions to adopt safe food storage practices in older adults
Bibliographic record
Abstract
Purpose The purpose of this paper is to apply the theory of planned behaviour (TPB) to determine which psychosocial factors are predictors of older adults’ safe food storage practices at home. Design/methodology/approach An online structured questionnaire was developed and administered to older adults (60+). Two behavioural intention outcomes were investigated: thawing meats safely and storing leftovers within recommended guidelines. The survey instrument measured socio-demographic and TPB variables: attitudes, subjective norms, perceived behavioural control and intentions. A measure of self-reported habitual behaviour was also recorded and used to determine whether past practice influenced behavioural intentions. Findings Respondents (n=78) demonstrated good intentions to safely defrost meats and store leftovers. The models accounted for 41 and 48 per cent of the variance in intentions to perform safe storage behaviours. Attitudes and subjective norms were predictors of intentions to safely thaw meats. Habitual behaviour was a significant predictor of behavioural intentions to safely store leftovers. Perceived behavioural control was a significant predictor of intentions to thaw meats and store leftovers. Research limitations/implications The sample size was small, and results are to be interpreted with caution. Practical implications The results indicate that theory-based solutions to solving food safety among consumers may be a feasible strategy. Originality/value The study is the first of its kind to apply the TPB to this consumer group.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".