Educational Quality of Services in Medical Universities of Islamic Republic of Iran: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The comparison between customers’ expectations and real provided services is defined as the quality of service. In medical education system, the negative quality gap can threaten the lives as graduates are probably not capable of managing the health condition of their patients. The aim of this study was to demonstrate a whole picture for educational quality of services in medical universities of the Islamic Republic of Iran.\n\nMethods: Persian databases (SID, Elmnet, Magiran, and IranMedex) and English electronic databases including Scopus and PubMed were searched (from 2005 to 2017). Our main search terms include medical university, SERVQUAL, and quality of education. The methodological quality was assessed by a modified Newcastle–Ottawa Scale (NOS). Information was gathered for the following terms: author, publication year, keywords, and main conclusion. The main outcome measurement was the measured gap for tangibles, reliability, assurance, responsiveness, and empathy dimensions along with the total educational services quality gap. Pooled difference in means (95 CI%) were evaluated. All analyzes were performed using comprehensive meta-analysis software.\n\nResult: For this study, 143 cross-sectional studies were found for review. Based on the random effect models, total weighted mean difference (WMD) was -1.23 (95 CI%: -1.35 to -1.11), WMD of assurance was -1.24 (95 CI%: -1.41 to -1.08), WMD of reliability was -1.04 (95 CI%: -1.28 to -0.80), WMD of responsiveness was -1.38 (95 CI%: -1.52 to -1.24), WMD of tangible was -1.25 (95 CI%: -1.41 to -1.10), and WMD of empathy dimension was -1.18 (95 CI%: -1.34 to -1.03). Stratified analysis revealed that if universities types and quality of studies decreases, all dimensions of the quality gap would be deteriorated.\n\nConclusion: Negative gap was reported for all faculties/universities. Students are the main customers of universities; hence items that are requested by students should be offered. Determining where gaps lie in different dimensions can guide the allocation of financial resources in education systems, in addition to improving decision-making and strategic planning.\n\nKeywords: QUALITY OF SERVICE, SERVAQUAL, EDUCATION, SYSTEMATIC REVIEW, MEDICAL UNIVERSITY
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".