Collaborating to Embrace Evidence-Informed Management Practices within Canada's Health System
Bibliographic record
Abstract
In late 2005, 11 major national health organizations decided to work together to build healthier workplaces for healthcare providers. To do so, they created a pan-Canadian collaborative of 45 experts and asked them to develop an action strategy to improve healthcare workplaces. One of the first steps taken by members of the collaborative was to adopt the following shared belief statements to guide their thinking: "We believe it is unacceptable to fund, govern, manage, work in or receive care in an unhealthy health workplace," and, "A fundamental way to better healthcare is through healthier healthcare workplaces. This commentary provides an overview of the Quality Worklife-Quality Healthcare Collaborative action strategy. This strategy embraces the thinking set out by the lead papers in a recent Special Issue of Healthcare Papers (www.Longwoods.com/special_issues.php) focused on developing healthy workplaces for healthcare workers, and brings to Life evidence-informed management practices.
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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 | 0.154 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.028 | 0.008 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".