A critical appraisal of what organisational approaches are pivotal to improve patient safety
Bibliographic record
Abstract
Background: Patient safety remains a priority for healthcare organisations globally. There remains little consensus regarding the extent of this issue and the resultant impact on both individuals and communities. Aim: Our study aims to provide healthcare organisations and decision makers with increased information regarding predictive risk factors to enhance patient safety, and develop an organisational culture of safety. Methods: This paper reviews current literature regarding patient safety and presents predictive risk factors and recommendations for healthcare organisations globally to measure and monitor patient safety. Results: Three categories of organisational factors promoting safety culture were identified – Focusing on system/culture, management support and team work and event reporting. Conclusions: This review strove to identify and discuss the predictive risk factors for patient safety and support the importance of a positive organisational culture and strong leadership in monitoring and reducing patient care errors and improving patient care in healthcare setting.
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 | 0.141 | 0.451 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".