Working Time Society consensus statements: Individual differences in shift work tolerance and recommendations for research and practice
Bibliographic record
Abstract
There is no standard definition of shift work universally, and no validated report of complete biological adjustment to shift work in workers. Similarly, the evidence for shift work tolerance is limited due to a small number of studies and a narrow range of outcome measures. This paper discusses evidence to date regarding individual differences in shift work tolerance and highlights areas for future research and recommendations for workplace practice. The few factors that are consistently associated with perceived or actual shift work tolerance are young age, low scores of morningness or being a late chronotype, low scores of languidity and neuroticism, high scores on extraversion, internal locus of control and flexibility and male sex. An important first step is to differentiate between factors that are potentially modifiable, such as those that are determined by lifestyle choices, and those factors specific to the working time arrangement. Identifying determinants of shift work tolerance and the ability to adjust to shift work, whether they are innate and/or acquired mechanisms, is important so workers who are less likely to tolerate shift work well can be self-identified and supported with appropriate harm/risk minimization strategies. This paper also identifies important areas for future research with the goal of increasing the evidence base on which we can develop evidence-based harm mitigation strategies for shift workers.
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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 | 0.082 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".