Teamwork and Healthy Workplaces: Strengthening the Links for Deliberation and Action through Research and Policy
Bibliographic record
Abstract
The two lead articles for this issue by Shamian and El-Jardali and by Clements, Dault and Priest provide an opportunity to consider how two agendas - teamwork in healthcare and the healthy workplace - can be strengthened to gain mutual advancement. Both agendas are in the pan-Canadian Health Human Resource (HHR) strategic plan in Canada and were also identified within the Health Council of Canada's 2005 Annual Report. Strong links have yet to be made related to the teamwork in healthcare agenda and its relationship with the workplace environment. Significant research has been conducted, and advocates are pushing for policy change. It is recommended that those engaged in the research in these two domains dialogue with each other and collectively consider ways in which they could advance the policy directions required to enhance both patient and provider satisfaction in our healthcare system. The teamwork and healthy workplace agendas require thoughtful deliberation between researchers and policy-makers to inform action. This commentary provides an example of how the Ontario government has been able to engage within an evidence-informed process to develop inter-professional care that may ultimately positively impact the teamwork in healthcare agenda and the healthy workplace agenda in the future.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.042 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.111 | 0.082 |
| Insufficient payload (model declined to judge) | 0.007 | 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".