Bibliographic record
Abstract
Lowe and Chan's proposal for the development of common work environment metrics is long overdue. The authors' healthy work environment (HWE) framework is evidence based and illustrates the relationships between HWEs and organizational-level outcomes in a succinct yet comprehensive manner. The challenges we face in implementing their framework are related not so much to a fear of change but to a willingness to engage with multiple stakeholders and levels of government in coordinating our efforts. To date, we have lacked, at the policy level, a belief that HWEs can reduce operating costs, improve human resource utilization and, ultimately, lead to higher-quality patient care. We need a framework that will allow us to compare organizational performance in the area of health human resources in the same manner as we compare organizational outcomes in other areas. Such comparisons would allow us to further our understanding of the relationships among care providers, workplaces and organizational outcomes.
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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.039 | 0.031 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".