Knowledge mapping of hospital accreditation research: a coword analysis
Bibliographic record
Abstract
PURPOSE: Hospital accreditation has been adopted internationally as a way and solution for healthcare quality improvement in hospitals. The purpose of this study was to review and knowledge mapping of bibliographic data about "Hospital Accreditation" and assess the current quantitative trends. DESIGN/METHODOLOGY/APPROACH: Scientometric methods and knowledge visualization using the coword analysis techniques conducted in three steps based on the data related to the field of hospital accreditation from 1975 to 2018 obtained from the MEDLINE database. Bibliographic data for titles, abstracts and keywords articles were saved in CSV format and MEDLINE templates by applying filters. Data extracted were exported into an Excel spreadsheet and were preprocessed. The authors applied the text mining and visualization using VOSviewer software. FINDINGS: Hospital accreditation studies have been increased rapidly over the past 30 years. 6,661 documents in the field of hospital accreditation had been published from 1975 to 2018. Hospitals or organizations active in the field of hospital accreditation were in the United States, Italy and Canada. The 10 most productive authors identified in the area of hospital accreditation with a higher influence were identified. "The United States", "accreditation", "Joint commission on accreditation" and "quality assurance, healthcare" had, respectively, the highest frequency. The cluster analysis identified and categorized them into four major clusters. Hospital accreditation field had a close relationship with the quality improvement, patient safety, risk and standards. ORIGINALITY/VALUE: Hospital accreditation had focused on the scopes of implementation of accreditation programs, adherence to JCI standards, and focus on safety and quality improvement. Future studies are recommended to be conducted on design interventions and paying attention to all dimensions of hospital accreditation.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.058 | 0.058 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".