Politics and Partnerships: Challenges and Rewards of Partnerships in Workplace Health Research in the Healthcare Sector of British Columbia, Canada
Bibliographic record
Abstract
In British Columbia (BC), Canada, a partnership of researchers, healthcare employers, and healthcare unions reduced high injury rates through examining determinants of healthy workplaces and designing, implementing, and evaluating interventions. Over 51 million dollars (Canadian) was saved from the BC healthcare budget over two years, largely attributable to the collaborative effort. Challenges and rewards of the process were determined from interviews and workshops with researchers and community stakeholders, and by obtaining direct input to this report. Challenges included maintaining communication and trust between partners, preserving partnerships during restructuring and labor disputes, and maintaining involvement and support of front-line workers and senior management. As all partners recognized the importance of the research agenda, the stakeholders remained committed to working through the challenges, and have consequently achieved considerable success.
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 | 0.110 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.045 | 0.031 |
| Scholarly communication | 0.037 | 0.010 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.007 | 0.008 |
| 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".