Work disability following major organisational change: the Whitehall II study
Bibliographic record
Abstract
BACKGROUND: Privatisation and private sector practices have been increasingly applied to the public sector in many industrialised countries. Over the same period, long-term work disability has risen substantially. We examined whether a major organisational change--the transfer of public sector work to executive agencies run on private sector lines--was associated with an increased risk of work disability. METHODS: The study uses self-reported data from the prospective Whitehall II cohort study. Associations between transfer to an executive agency assessed at baseline (1991-1994) and work disability ascertained over a period of approximately 8 years at three follow-up surveys (1995-1996, 1997-1999 and 2001) were examined using Cox proportional hazard models. RESULTS: In age- and sex-adjusted models, risk of work disability was higher among the 1263 employees who were transferred to an executive agency (HR 1.90, 95% CI 1.46 to 2.48) compared with the 3419 employees whose job was not transferred. These findings were robust to additional adjustment for physical and mental health and health behaviours at baseline. CONCLUSIONS: Increased work disability was observed among employees exposed to the transfer of public sector work to executive agencies run on private sector lines. This may highlight an unintentional cost for employees, employers and society.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".