An integrated literature review on cultural management systems and patient safety
Bibliographic record
Abstract
The incidence of adverse events in healthcare is a global problem with negative consequences for all stakeholders including patients, their family members, health professionals and the government. Patient safety and patient safety culture lie at the heart of all adverse events within healthcare settings. The culture of an organization determines its approach to problem solving and determines how individuals within that setting work; this is also true for patient safety culture and the reduction of adverse events within healthcare organizations. The aim of this study was to assess, identify and have a better understanding of the importance of patient safety culture within the healthcare organization and to create insights on the impact of cultural management systems regarding patient safety. The research method of this study is an integrated literature of the patient safety culture and perspectives of healthcare workers, assessed using the Modified Stanford Instrument (MSI) and Manchester Patient Safety Framework (MaPSaF). Analysis of the data revealed that health professionals working in the same organizations have differing opinions on the same topic; therefore, there is need for open communication and a systematic approach to establishing the right safety culture within healthcare organizations. In conclusion, establishing the right culture and having systematic ways of measurement enable improvements and the ability of organizations to learn from their mistakes. There is paucity of data with respect to the use of these tools in the respective countries (Canada and United Kingdom) even though the tools are the national tools established through rigorous research. Therefore, a study of MaPSaF in New Zealand was also analyzed. There is need for further research and publications to enable learning on patient safety, which will reduce the incidence of adverse events and associated consequences in healthcare organizations.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".