A mapping review of challenges in existing technology-based occupational safety training in the tourism and hospitality industry: Research potential in commercial kitchens
Bibliographic record
Abstract
Introduction: An effective response to a safe and healthy work environment relies on advanced preparedness such as occupational safety training. The main objectives of this study are to describe and classify the most critical challenges and identify knowledge gaps in the literature that could inform future research. Methods: A systematic mapping review gathers information from six search engines; Francis and Taylor, Scopus, Science Direct, Emerald Insight, and SpringerLink, which yielded journal publications between 1948 and 2022. The data were analyzed using meta-analysis from 135,310 article search results, whereby 20 articles met the inclusion criteria. The studies varied in terms of aim, study design, and reporting detail. Results: The results showed that Canada and the United States are countries that study safety training in the tourism or hospitality industry, mainly in food management and food safety. The results also show that studies on occupational safety training in commercial kitchens are not common in the existing literature. The findings revealed that the highest number of articles involving safety training focused on food safety and food management in the tourism industry but less on kitchen workers' safety. Conclusions: This mapping review demonstrates hospitality workers' struggles, especially commercial kitchen workers. This review presents the types of technologies used for occupational safety training and provides an overview of different strategies that address the challenges. Among the most significant obstacles in occupational safety and health training are a lack of knowledge, high financial costs for implementation, and outdated policies from authorities.
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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 | 0.021 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".