The Challenge of Effective Workplace Change in the Health Sector
Bibliographic record
Abstract
There is significant personal injury risk associated with the provision of high-quality healthcare. The magnitude of this risk, combined with the possibility that it can often go underappreciated by caregivers and the organizations they work for, might help explain why the health sector has largely missed out on the benefits of an overall declining trend in injury rates. Despite covering two very different topics in their lead papers, Shamian and El-Jardali and Clements, Dault and Priest present a surprising degree of overlap in relation to what might help enable effective workplace change. Leadership, role clarity, trust, respect, values and workplace culture are all viewed as key enablers of effective teamwork by Clements, Dault and Priest. They could also be considered required ingredients of successful workplace health initiatives, as discussed by Shamian and El-Jardali. A lot of background and positional work regarding teamwork and healthy workplaces exists, but this has not necessarily translated into front-line change. These authors have done an excellent job of pointing out the potential benefits of workplace changes. What is needed now is for someone to take the lead in developing, implementing and evaluating these changes. The adult human form is an awkward burden to lift or carry. Weighing up to 200 pounds or more, it has no handles, it is not rigid, and it is susceptible to severe damage if mishandled or dropped. When lying in bed, a patient is placed inconveniently for lifting and the weight and placement of such a load would be tolerated by few industrial workers.
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 | 0.062 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.027 | 0.017 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".