Identifying research challenges for occupational and environmental medicine until 2030: an initiative
Bibliographic record
Abstract
> The public ultimately provides money for medical research for one purpose only – to generate improvements in patient care [and public health] Medical researchers, editors, and peer reviewers should be under no illusions: the public does not support research for the pleasure of watching a cultural event If improved medical care [and improved public health] is not delivered, support for medical research . will dwindle and atrophy. > > DF Horrobin1 In recent years it has been suggested that the multifaceted fields of occupational and environmental medicine are facing problems as disciplines. Jack Siemiatycki2 addressed several of the issues concerned when he focussed on the “future of occupational epidemiology” in a keynote speech at the 2007 EPICOH conference in Banff, Canada. One of his critical observations was that “over the past 20 years, occupational epidemiology has declined as regards its relative share of the epidemiological pie”. In a similar vein, it seems appropriate for some, if not many, of us to ask whether occupational and environmental medicine has lost its relative and due share of the “medical pie”. While most representatives of our disciplines would agree that what we do is relevant for public health, there is increasing uneasiness as to whether our objectives and achievements are appropriately visible beyond our circles. Moreover, there is the disconcerting question of whether there is really consensus among, let alone beyond, ourselves as to what constitutes the important research issues lying ahead of us during the next, say, two decades on a global scale. And yet, it is very important to identify the challenges for occupational and environmental medicine and to ask ourselves why society should pay for our research for two main reasons:
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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 | 0.160 | 0.102 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.020 | 0.029 |
| Scholarly communication | 0.042 | 0.058 |
| Open science | 0.009 | 0.039 |
| Research integrity | 0.064 | 0.047 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".