Bibliographic record
Abstract
Most readers of this journal would recognise that occupational health (OH) research is valuable. The Society of Occupational Medicine (SOM), a UK-based organisation for healthcare professionals working in or with an interest in OH, recently released a report where the objective was to assess the value of OH research.1 To do this, they undertook a scoping review of economic evaluations of OH interventions (one aspect of OH research) and conducted a series of nine interviews with academic experts, OH providers and representatives from employers and governments in the UK and internationally. Based on these activities, they concluded that, while there is a strong case supporting the societal and public value of OH research, there is a lack of high-quality intervention studies that demonstrate the economic value of OH interventions. The report also provides nine recommendations which emphasise: the need for leadership and coordination of OH research both in the research agenda and the dissemination of research findings; developing and expanding the OH research workforce; using new technologies; and placing more emphasis on gathering data which shows the value of OH research. The SOM report comments on the decreasing size of the OHS research workforce, and the ageing of the current cohort of researchers. The authors also lament the lack of clear pathways to a career in OHS research. We note that these challenges are …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.201 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 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".