Employment and mental health among UK ex-service personnel during the initial period of the COVID-19 pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic interrupted participation in the labour force and may have affected mental health, both directly through the effects of illness and isolation and indirectly through negative effects on employment. Former military personnel may be at particular risk as a result of both additional exposure to risk factors for poor mental health and barriers to labour market participation raised by the transition from military to civilian working environments. This article examines furlough and unemployment as a result of the COVID-19 pandemic among UK working-age ex-service personnel and its associations with poor mental health. Methods: Participants from an existing cohort study of Iraq- and Afghanistan-era UK Armed Forces personnel were invited to provide information on employment before the COVID-19 pandemic and how it has changed since the pandemic. Mental health was measured using the General Health Questionnaire and compared with data collected pre-pandemic. Results: Although Veteran unemployment is not higher than civilian unemployment (4.7% and 4.8%, respectively, in September 2020), it rose during the pandemic from a lower level (1.3%). Part-time and self-employed Veterans were more likely than full-time employees to experience furlough or unemployment. A negative impact on employment was associated with the onset of new mental ill health. Discussion: Employment of ex-service personnel was more negatively affected by the COVID-19 pandemic, possibly because ex-service personnel are mostly men, and men were more affected in the UK general population. This employment instability has negative consequences for mental health that are not mitigated by furlough.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".