The perception of healthcare employees and the impact of healthcare accreditation on the quality of healthcare in Korea
Bibliographic record
Abstract
Objective: In order to encourage more hospitals to participate in the accreditation, there needs to be “substantial evidence of the effectiveness of accreditation”. The aim of this study was to identify and analyze healthcare employees’ perceptions of hospital accreditation and the impact of hospital accreditation on the quality of healthcare in Korea.Methods: Eight electronic databases were searched between June and July 2016. Of the initially identified 392 abstracts, 14 empirical studies on healthcare accreditation in Korea were selected based on the inclusion criteria. These were retrieved and analyzed.Results: The 14 studies assessed healthcare employees’ perception of hospital accreditation as well as the impact of hospital accreditation on the quality of healthcare. The results were classified into four categories according to perception (Need, Purpose, Intent, and Relevance of standards), and into five categories according to the impact of accreditation (Patient safety and healthcare quality, Satisfaction with hospital employees, Leadership, Organizational culture, and Managerial performance). Findings showed that healthcare employees’ had good understanding of the purpose, need, and intention of the healthcare accreditation system, but indicated that limitations exist with the accreditation standards. Moreover, evidence showed that healthcare accreditation in Korea has made a positive impact on “patient safety and healthcare quality”, “leadership” and “organizational culture”.Conclusions: Healthcare accreditation has had a positive overall impact on hospitals and has improved the quality of healthcare as well as patient safety. However, more rigorous research and more diverse research methods are required to determine its long-term effect.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".