Bibliographic record
Abstract
Healthcare has failed to make the same progress as other high-risk industries when it comes to creating safety - despite over a decade of research, education and implementation of safety systems in health services. Safe care is created by systems and standardization, and also by proactive, thinking staff working in partnership with consumers and each other; but the healthcare industry appears to struggle to reconcile these concepts. Even with our evolved knowledge of how human beings operate in organizations, and the best intentions, the dominant change paradigm in healthcare is still hierarchical, based on top-down policies implemented by managers and staff. Although the power spread in health services is being tested through generational change, we have a long way to go before proactivity and initiative at the front line are universally fostered and welcomed by healthcare managers and senior clinicians. "Front Line Ownership: Generating a Cure Mindset for Patient Safety," by Zimmerman et al., presents a persuasive example of how staff ownership of improving consumer safety is a powerful tool for change, one that deserves its place at the front line of safety and quality improvement methods.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.076 | 0.070 |
| 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".