Bibliographic record
Abstract
On 15 May 2008, a debate in the House of Lords highlighted the economic costs of what was termed ‘mental ill health’ in the workplace. Drawing from reports by the Sainsbury Centre for Mental Health, Baroness Neuberger highlighted the total per annum costs of employee mental ill health as nearly £26 billion (equivalent to £1 000 per employee in the UK). This fi gure is computed from sickness absence, the costs of staff replacement and the substantive impact of reduced productivity of those present, but unwell, at work. Particular reference was made to people employed by the NHS, where the Baroness spoke of numerous people who had confided in her that they had mental health difficulties but were unable to speak of these to their managers. Furthermore she noted that ‘mental ill health among staff costs the NHS over £1 billion, which is equivalent to a quarter of the entire mental health budget for England’ (House of Lords, 2008).
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.020 | 0.022 |
| Insufficient payload (model declined to judge) | 0.082 | 0.011 |
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".