The health and safety impacts of night working: the case of TSSA workers
Bibliographic record
Abstract
Official figures report that over one quarter (27%) of the UK workforce, or roughly 8.7 million people, were night-time workers in 2022.1 While over half of night-time workers are male (56%), in the past decade the number of female night-time workers has increased. In 2017 19% of those in employment were engaged in shift work, 20% of males and 17% of females (ONS, 2018). There are proportionately more people from Black and Minority Ethnic backgrounds working at night, compared to the overall UK workforce (Young Foundation, 2011), while between 2012 and 2022, the number of night-time workers born outside of the UK rose by 33% to 2.0 million (ONS,2022). The UK HSE states that ‘only a limited number of workers can successfully adapt to night work’ since night shifts disrupt the internal body clock (2006). A substantial international body of work has demonstrated that night and shift work are linked to a wide range of mental and physical health conditions (Torquati, et al., 2019; Moreno, et al., 2019; Gurubhagavatula, et al., 2021). These conditions have significant effects on workers, but also their relationships, families and social lives (Arlinghaus, 2019). Managerial and organisational responses to these trends have largely been to suggest that the impacts of night work on the health of workers may also or instead be explained by lifestyle habits or personal preferences; an argument which deflects responsibility from employers and their duty of care, onto individual workers. This report provides a review of the literature on the impact of night and shift work on workers’ health and their families and an analysis of night work premia, based on rates provided by the Labour Research Department. It then sets out research exploring workers’ experience of night work and the impact it has upon their lives, the factors that shape their decision-making about night work, how both organisational and labour market factors and changes shape night work and the measures that employers and unions take to mitigate risk. The overall research covers five unions: The Communication Workers’ Union (CWU), The Rail and Maritime Transport Union (RMT), The Transport Salaried Staffs' Association (TSSA), Equity and Community. It is based on interviews with a national officer from each union and 55 union members working night shifts in a range of workplaces and job roles. The research aimed to: i) Examine the experiences and perceptions of night working, including on-call work and its impact on the physical and mental health of workers. ii) Explore the impact of changes in work, regarding both organisational psychosocial risks including workloads, supervisor and social support, job cuts and vacancies as well as the labour market level, for example, outsourcing, on experiences of night working. iii) Interrogate workers’ preferences for night work and the factors that may influence their reasons for undertaking night work. iv) Develop potential union demands relating to shift work and night work. These demands may exist as standalone issues or within the context of longer-term demands for a shorter working week.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".