The Role of Organizational Culture and Climate for Well-Being among Police Custody Personnel: A Multilevel Examination
Bibliographic record
Abstract
United Kingdom Police custody is one of the most challenging of work environments, liable to excessive demands and reduced well-being. Being difficult to access, it is also a much-neglected area of research that has focused on one or two roles, rather than the full range available, and on individual-level research, rather than a more comprehensive multilevel understanding of how organizational culture and climate can simultaneously influence a range of well-being outcomes. The present longitudinal study explored all types of roles, in both the public and private sectors, across seven English police forces and 26 custody sites (N = 333, response rate 46.57%, with repeated returns = 370). The Integrated Multilevel Model of Organizational Culture and Climate (IMMOCC) was applied to examine the organizational-level influences on individual well-being. Results indicated that (1) custody sergeants were most vulnerable to low well-being, followed by publicly contracted detention officers; (2) shared leadership (a source of team cohesion) was linked to four of six well-being outcomes; (3) two sub-components of culture reflected tensions never acknowledged before, especially in respect of role; and (4) reverse relationships existed between well-being outcomes and the dimensions of culture and climate. The findings inform practical recommendations, including resilience training and the need to raise the status of police custody, while also highlighting concerns about private sector scrutiny that may be relevant to other professions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".