Bibliographic record
Abstract
Numerous initiatives have been developed to create healthy workplaces in healthcare settings.However, despite these efforts nurses continue to experience negative conditions in their work settings and report challenges to maintaining physical and mental health.Stronger incentives must be put in place to ensure that current healthcare settings meet evidence-based standards for healthy work environments.The authors of these two papers provide us with a good overview of healthy workplace issues and describe various initiatives that have been implemented in Canadian healthcare settings in recent years.They focus on two priorities established by the Office of Nursing Policy in Health Canada and championed by Dr. Judith Shamian and Dr. Sandra MacDonald-Rencz -healthy nursing workplaces and effective interdisci-plinary teamwork.Shamian and El-Jardali provide a convincing array of research findings to support the need for these initiatives.It is helpful to see a collation of these various programs in a single article, and it clearly demonstrates that healthy workplaces are on the current policy agenda.The authors outline a number of recommendations for research, policy, practice and education to take this work to the next
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 | 0.027 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.113 | 0.104 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".