An evaluation of an occupational health advice service
Bibliographic record
Abstract
Objective: The objective of this article is to identify the profile of service users of an occupational health (OH) support service and establish areas of need, and to gather client feedback on the experience of participating in the support service and perceived outcomes and the impact of the advice received. Design and Setting: We carried out quantitative analyses of the characteristics of the service users and a quantitative survey of service users of GP surgeries and public events. Method: During 717 contacts with workers via opportunistic consultations, OH advisers collected participant information and details of occupational histories. To establish the outcomes and impact of the service, a telephone survey was conducted with a convenience sample of 165 participants 3 months after the initial consultation. Results: Contacting clients through opportunistic meetings was a useful way of reaching a cross section of workers and delivering OH support and advice. Overall, there was apparent need for OH support amongst employees from all sectors and sizes of workplace. The research identified that more than a third of clients reported work-related health conditions and more than 10 per cent had taken work-related sickness absence over the previous 12 months. More than 70 per cent of those that provided feedback rated the discussion as useful or very useful. The reported benefits were that the consultations led to perceived improvements in knowledge and confidence for tackling OH issues for the majority of respondents. More than a quarter of respondents reported taking actions to improve their work situation after consultation with the adviser. Conclusion: Work-related illnesses can be addressed through support services that provide employees with independent OH advice and guidance. Furthermore, there is an apparent need for employee OH support that is based outside of the workplace.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".