Validation of factors identified as influencing employee adherence to food safety management systems: a meat industry perspective
Bibliographic record
Abstract
This thesis is an investigation of multi-level influences on worker behaviour. The purpose of this study was to develop and validate a model that identifies factors which influence workers to follow food safety management systems (FSMSs) in meat plants in Ontario. A two-stage approach was employed. The qualitative phase involved 27 in-depth interviews at nine meat processing establishments and three focus group interviews with industry experts, as well as in-plant observations and reviews of records in five establishments. In-depth and focus group interviews generated 405 single-spaced pages of verbatim transcriptions. Transcripts were content-analyzed using NVivo 8 software. These data were supplemented with field notes from observations and reviews of plant records. Findings supported by literature from food safety and other fields were used to develop and administer a written survey for the quantitative study. Using single-stage cluster sampling, 471 surveys from personnel at four further-processing establishments, a 91% response rate, were analyzed in Mplus 6.1 using structural equation modelling, specifically, path analysis. The qualitative phase identified six main themes--management commitment to food safety (MC), food safety training (FST), supervisory practices, peer/co-worker influence, worker attitude and routines, and organizational/situational factors--as factors influencing worker adherence to FSMSs. The quantitative phase identified that supervisor commitment and worker commitment to food safety contribute to work unit commitment to food safety (WUC). It demonstrated that WUC is the only factor predicting worker food safety behaviour (WFSB). MC has a strong positive direct effect on WUC; this relationship is partially mediated by FST and an infrastructure supportive of food safety. Survey respondents were from large, unionized processing establishments. Results may not be generalizable to smaller or non-unionized meat plants or different processing sectors. Interventions to improve food safety that are targeted to the strongest predictive factors may yield more cost efficient improvements in food safety behaviour than interventions targeted to other factors. Management commitment to food safety is thought to be a key factor influencing worker food safety behaviour in meat processing establishments. No previous study has empirically shown this relationship as being fully mediated by work unit commitment.
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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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".