Healthy Workplaces and Teamwork for Healthcare Workers Need Public Engagement
Bibliographic record
Abstract
This response challenges the healthcare system to take full responsibility for the work environments created for health human resources. While the need for healthy work environments and teamwork in healthcare are inarguable, the fact is they are not a reality in today's health system. The authors suggest strategies to address this issue and identify the person or groups that should take responsibility, including governments, organizations, individuals and the public. Strategies include ensuring that policies do not contradict one another and holding each level responsible for the outcomes of a healthy work environment - retention and recruitment of health human resources, better patient/client outcomes and healthcare costs. The need for strong and appropriate leadership for health human resources with "content knowledge" is discussed, along with recommendations for measuring the performance and success of healthy work environments and teamwork. The authors conclude that collaboration at the micro, meso and macro levels is required to facilitate the true change that is needed to improve the work environments of health human resources.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.058 | 0.049 |
| 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".