Academic hospitals in the Toronto region collaborate to optimize occupational health and safety
Bibliographic record
Abstract
In March 2020, as the COVID-19 cases began to rise in Ontario, Canada, the central role of Occupational Health and Safety (OHS) to ensure the well-being of hospital workforce became highly visible. While Ontario's hospitals concentrated efforts to meet each challenging and uncertain wave stressing the system, it was apparent that there is a lack of consistency in best practices and policy response across the healthcare sector. Additionally, the unprecedented pressure on healthcare workforce as they attempted to meet the pandemic's new surging demands resulted in workforce shortages and increased levels of burnout, making it difficult to engage, support, and retain the staff necessary for delivering highest quality of services. The Toronto Academic Health Science Network (TAHSN), a dynamic consortium of 14 healthcare organizations, established a collaborative to implement an integrated effort and align on structure, processes, and standards that will increase strength and defensibility of TAHSN programs. To foster community building, identify areas of common concern, and co-create practices during and beyond the COVID-19 pandemic, a structured network of 14 OHS directors across the healthcare organizations was established. This article discusses the origin of the TAHSN collaborative, the thriving community vision for partnership, and the case study methodology used to combine capabilities to showcase innovation and excellence in care together.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".