Challenges in Patient Discharge Planning in the Health System of Iran: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: One of the main factors relating to quality of hospitals is effective discharge planning. Discharge planning promotes the quality of inpatient care and reduces unplanned hospital readmission. The current study investigated the challenges of discharge planning observed in the health system of Iran. METHODS: This qualitative research was conducted using a thematic and framework analyses to identify the challenges under each themes defined by the World Health Organization (WHO), to understand barriers in developing an effective discharge planning system in Iran health system. The data was collected from detailed semi-structured interviews and sessions of focus group discussions. This study involved 51 participants including health policy makers, hospital and health managers, faculty members, nurses, practitioners, community medicine specialists and other professionals of the Ministry of Health and Medical Education (MOHME). To reduce the bias and to increase the credibility of the study, evaluation criteria from Lincoln and Guba were used. All interviews and FGDs were recorded and transcribed, then analyzed by the software MAXQDA-11 and also manually. RESULTS: According to the WHO health systems framework, challenges of effective hospital discharge planning were divided into six areas, leadership/governance, service delivery, information, financing, health workforce, and medical production(themes), in which there were 5,3,2,2,3,1 subthemes respectively. CONCLUSION: It is evident from the findings of this study that changes in the perspective of policy makers, health staff and managers, strengthening of systematic approach, and establishment of required infrastructures are essential for successful implementation of effective discharge planning in health systems in Iran.
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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 | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".