Background Factors Affecting the Implementation of Food Safety Management Systems
Bibliographic record
Abstract
A peer-reviewed article Why don’t workers follow Hazard Analysis Critical Control Point (HACCP) guidelines? Socio-psychological models have been used to describe factors that influence the implementation of food safety management systems (FSMSs) in food processing facilities. The theory of planned behavior posits that perceived control over one’s own behavior, one’s attitude and the influence of others are antecedents of behavioral intention and/or behavior. The objectives of this study were to identify background factors that influence food safety behaviors of production workers in small and medium sized meat processing facilities and examine how these factors are applicable to the theory of planned behavior. Using a qualitative approach, the researchers conducted 13 in-depth interviews at five meat plants and two focus group interviews with representatives of government and industry agencies. These interviews generated 219 single-spaced pages of verbatim transcripts, which were analyzed by use of NVivo 7 software. Ten themes found in the data relate to elements in the theory of planned behavior that were demonstrated to be applicable to a meat processing establishment. Confirmation of factors having the strongest influence on production workers in meat plants may assist in developing targeted interventions that improve the implementation of FSMSs in the meat and other food processing sectors.
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 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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".