Healthy Workplaces: The Case for Shared Clinical Decision Making and Increased Full-Time Employment
Bibliographic record
Abstract
Today, healthy work environments are recognized as essential to attain positive experiences and optimal clinical outcomes for patients, the well-being of healthcare providers and organizational effectiveness. Creating such environments is both a collective and an individual responsibility. It requires each of us to move away from the rhetoric, abandon our comfort zones and territorialities, adopt new evidence, and fully embrace the collective good. This commentary builds on the two excellent papers on this issue (Shamian and El-Jardali, and Clements, Dault and Priest), and adds two new necessary elements to build healthy workplaces and productive teamwork. The first is shared clinical decision making, the most substantive form of teamwork, and a necessary condition to build healthy work environments and deliver optimal patient care. The second is employment status: we cannot achieve healthy work environments and optimal teamwork with overreliance on part-time, casual or agency employment. The key premise for Ontario's 70% full-time employment policy is based on the fact that such a percentage is a necessary, minimal condition to ensure continuity of care and caregiver for patients, and continuity of relationships for our teams.
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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.079 | 0.076 |
| Insufficient payload (model declined to judge) | 0.005 | 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".