Pathologist workload, work distribution and significant absences or departures at a regional hospital laboratory
Bibliographic record
Abstract
OBJECTIVE: Assess the work environment of salaried pathologists via (1) the national workload system (L4E), (2) work distribution among/in three hospital groups, and (3) the frequency of significant absences or departures (SADs). METHODS: Automated analysis of pathology reports from a regional laboratory (accessioned 2011-2019) using validated computer code. RESULTS: The study set contained 574,099 pathology reports, reported by 63 pathologists. The average yearly L4E workload units/full-time equivalent for three hospital groups were 8,101.6, 6,906.5 and 4,215.8. The average Gini coefficient for full-time pathologists in the three hospital groups were respectively 0.05, 0.16 and 0.23. The average yearly SADs rates were respectively 13%, 16% and 9%. The group with the highest SADs rate had the intermediate Gini coefficient and intermediate workload. CONCLUSIONS: High individual workload and work maldistribution appear to be associated with SADs. Individual workload maximums and greater transparency may be essential for limiting staff turnover, maintaining high morale, and efficient laboratory function with a high quality of care.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".