Priority issues in occupational cancer research: Ontario stakeholder perspectives
Bibliographic record
Abstract
INTRODUCTION: Workers are potentially exposed to known and suspected carcinogens in the workplace, many of which have not been fully evaluated. Despite persistent need, research on occupational cancer appears to have declined in recent decades. The formation of the Occupational Cancer Research Centre (OCRC) is an effort to counter this downward trend in Ontario. The OCRC conducted a survey of the broad stakeholder community to learn about priority issues on occupational cancer research. METHODS: The OCRC received 177 responses to its survey from academic, health care, policy, industry, and labour-affiliated stakeholders. Responses were analyzed based on workplace exposures, at-risk occupations and cancers by organ system, stratified by respondents' occupational role. DISCUSSION: Priority issues identified included workplace exposures such as chemicals, respirable dusts and fibres (e.g. asbestos), radiation (e.g. electromagnetic fields), pesticides, and shift work; and occupations such as miners, construction workers, and health care workers. Insufficient funding and a lack of exposure data were identified as the central barriers to conducting occupational cancer research. CONCLUSION: The results of this survey underscore the great need for occupational cancer research in Ontario and beyond. They will be very useful as the OCRC develops its research agenda.
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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 | 0.057 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".