Hospital Accreditation: What Difficulties Does It Face in Iran?
Bibliographic record
Abstract
INTRODUCTION: To determine if an established programme is achieving desired goals and objectives, in other words being effective, health-care policy makers need to recognise and cope with its challenges. This paper made an effort to pinpoint the main difficulties which appear on the way to successful implementation of the Iranian hospital accreditation programme, from perspective of hospitals medical and clinical staff, and accreditation authorities.MATERIAL & METHODS: applying a qualitative approach, we used semi-structured discussion guide in Focus Group Discussions (FGDs), as well as semi-structured In-Depth Interviews (IDIs) with purposively selected hospitals staff and accreditation programme authorities. Data collection was conducted in Iranian universities of medical sciences from June to September 2014. In order to analyse collected opinions, thematic content analysis was applied independently by two authors.FINDINGS: In addition to four independent FGDs with 27 participants, conducting seven individual IDIs were enough to reach data saturation. A total of 25 subthemes were emerged under five main themes. Participants were of the opinion that the accreditation problems include fundamental deficiencies in the Iranian healthcare system, poor design of the programme, deficiencies within hospitals, difficulties in surveyors and survey processes and negative impacts of the accreditation on hospitals.DISCUSSION: difficulties with the accreditation programme arise from a wide variety of sources. Decision-makers’ achievements in the desired goals lie on recognizing and resolving them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".