Establishment of a national Canadian workplace exposure database: progress and challenges
Bibliographic record
Abstract
Objectives Workplace exposure databases are important tools in epidemiologic research and have been established in several countries. With the exception of radiation, a national occupational database has not existed in Canada, in part because workplace regulation is fractioned into multiple federal and provincial jurisdictions. As part of the CAREX Canada project, measured exposure data is being centralized in a Canadian workplace exposure database (CWED). Methods Fifteen Canadian regulatory jurisdictions were contacted with open-ended survey questions pertaining to their existing occupational exposure data (number of records, storage format, retention), sampling practices (historical time frame, yearly rate), and regulatory practices. Data were requested from all jurisdictions. Temporal, geographical, and industry-based data gaps were identified; efforts to fill these are underway. Results Initially, two provincial databases (N=70 000 and 110 000 samples) and one national radiation exposure database (N=150 000) were obtained for integration into the CWED, providing a solid base for exposure estimates. Although obtaining data has been more complicated than predicted, CAREX Canada9s evolving partnerships with other Canadian jurisdictions will ensure that some existing gaps are filled. The CWED is anticipated to grow from its current size of 330 000 entries to 430 000 entries by 2013. Conclusions The CWED will be a useful stand-alone tool for exposure related questions. It will also be an excellent source of information for a variety of end-users in the areas of primary prevention, research, and exposure and disease surveillance. Examples of the CWED9s utility to epidemiologic research and challenges associated with its development will be discussed.
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 | 0.086 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.016 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".