An occupational health survey of the UK’s mortuary workforce
Bibliographic record
Abstract
BACKGROUND: Mortuaries are predominantly staffed by anatomical pathology technologists (APTs) and pathologists, and the work they undertake carries implicit health risk due to its nature. Until now there has not been a nationwide assessment of the occupational health of these essential workers in the UK. AIMS: To assess the current occupational health status and needs of the mortuary workforce in the UK. METHODS: We created a bespoke, brief online survey which was approved by the professional bodies representing APTs and pathologists in the UK. The survey was disseminated electronically using these organizations' targeted mailing lists. RESULTS: Two hundred and thirty participants completed the survey, comprising 108 (47%) APTs and 122 (53%) pathologists. Most (89%) respondents reported that they have suffered from occupational health issues, the largest subcategory being musculoskeletal problems (77%). Almost half (48%) of APTs and around one-quarter (26%) of pathologists who responded have taken time off work in the last year because of occupational health problems, with almost one-fifth (19%) of the APTs having taken at least 4 weeks off. CONCLUSIONS: A significant number of workhours are lost per year to sick leave resulting from occupational health problems. Respondents' comments highlight issues in workspaces, rest facilities and staffing, and variability in working conditions across the country. We suggest that future workforce planning should prioritize good occupational health, with nationwide improvements in mortuary design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".