The nature and correlates of paid and unpaid work among service users of London Community Mental Health Teams
Bibliographic record
Abstract
Aims. Little is known about how the rates and characteristics of mental health service users in unpaid work, training and study compare with those in paid employment. Methods. From staff report and patient records, 1353 mental health service users of seven Community Mental Health Teams in two London boroughs were categorized as in paid work, unpaid vocational activity or no vocational activity. Types of work were described using Standard Occupational Classifications. The characteristics of each group were reported and associations with vocational status were explored. Results. Of the sample, 5.5% were in paid work and 12.7% were in unpaid vocational activity, (including 5.3% in voluntary work and 8.1% in study or training). People in paid work were engaged in a broader range of occupations than those in voluntary work and most in paid work (58.5%) worked part-time. Younger age and high educational attainment characterized both groups. Having sustained previous employment was most strongly associated with being in paid work. Conclusions. Rates of vocational activity were very low. Results did not suggest a clear clinical distinction between those in paid and unpaid activity. The motivations for and functions of unpaid work need further research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".