Economic evaluation of a psychological surveillance and support programme in the UK police force
Bibliographic record
Abstract
Purpose Police officers in the UK frequently experience traumatic events and work pressures, and these can have a detrimental impact on mental health. A psychological surveillance programme has been implemented and clinically evaluated. This paper reports an economic evaluation of the programme. Design/methodology/approach A decision analytic model was developed to explore programme costs, outcomes and return on investment for a notional cohort of 1,000 police personnel (officers and staff). The model parameters were obtained from a large data set of sequential psychological surveillance results. Changes in individual work productivity were derived from a workability measure included in the survey. Findings The modelled expected total cost incurred to screen 1,000 participants was £84,287 ($106,971). The expected net increase in work productivity for those receiving interventions was valued at £241,672 ($306,713). This result represents a return on investment of 187%. Sensitivity analyses showed that the model was robust to changes in key parameters. Research limitations/implications Police personnel experiencing traumatic events may need specialist support from occupational health services or psychological therapy. Screening or surveillance can detect mental health problems, but its cost-effectiveness has been previously unknown. Practical implications The value for money of providing surveillance within the police force to detect mental health problems is assessed in this research. The gains in terms of increased work are evident. In similar environmental working conditions, this could extend to emergency services globally. Social implications The police are often called upon to deal with the lowest points in people’s lives. The findings have implications for police forces wishing to support officers to be able to provide the best of themselves in these situations. Originality/value There have been no previous studies in policing that have concluded that running a psychological surveillance programme in policing is financially viable. Although this requires initial investment, this paper models that the extra costs incurred are more than offset by improved work productivity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".