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Record W1982509638 · doi:10.12809/hkmj134063

Factors affecting implementation of accreditation programmes and the impact of the accreditation process on quality improvement in hospitals: a SWOT analysis

2013· review· en· W1982509638 on OpenAlexfundno aff
Gloria KB Ng, Gkk Leung, JM Johnston, Benjamin J. Cowling

Bibliographic record

VenueHong Kong Medical Journal · 2013
Typereview
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsnot available
FundersUniversity of Hong KongQueen Mary University of LondonUniversity of Ottawa
KeywordsAccreditationQuality managementSWOT analysisMedicineWorkloadQuality (philosophy)Health careBusinessMedical educationNursingPublic relationsPolitical scienceMarketing

Abstract

fetched live from OpenAlex

OBJECTIVES: The objectives of this review were to identify factors that influence implementation of hospital accreditation programmes and to assess the impact of the accreditation process on quality improvement in public hospitals. DATA SOURCES: Two electronic databases, Medline (OvidSP) and PubMed, were systematically searched. STUDY SELECTION: "Public hospital", "hospital accreditation", and "quality improvement" were used as the search terms. A total of 348 citations were initially identified. After critical appraisal and study selection, 26 articles were included in the review. DATA EXTRACTION: The data were extracted and analysed using a SWOT (strengths, weaknesses, opportunities, threats) analysis. DATA SYNTHESIS: Increased staff engagement and communication, multidisciplinary team building, positive changes in organisational culture, and enhanced leadership and staff awareness of continuous quality improvement were identified as strengths. Weaknesses included organisational resistance to change, increased staff workload, lack of awareness about continuous quality improvement, insufficient staff training and support for continuous quality improvement, lack of applicable accreditation standards for local use, and lack of performance outcome measures. Opportunities included identification of improvement areas, enhanced patient safety, additional funding, public recognition, and market advantage. Threats included opportunistic behaviours, funding cuts, lack of incentives for participation, and a regulatory approach to mandatory participation. CONCLUSIONS: By relating the findings to the operational issues of accreditation, this review discussed the implications for successful implementation and how accreditation may drive quality improvement. These findings have implications for various stakeholders (government, the public, patients and health care providers), when it comes to embarking on accreditation exercises.

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 imitation

Not 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.

metaresearch head score (Codex)0.095
metaresearch head score (Gemma)0.297
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.095
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.297
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0210.034
Science and technology studies0.0010.002
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.166
GPT teacher head0.579
Teacher spread0.413 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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".

Quick stats

Citations102
Published2013
Admission routes1
Has abstractyes

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