Strategies to improve the structure of primary health care in the villages of Iran: a qualitative study
Bibliographic record
Abstract
Background.Low-quality Primary Health Care (PHC) services reduce the effectiveness of care and public confidence in the health system.Objectives.This study aimed to provide strategies to improve the structure of primary health care in the villages of Iran.Material and methods.The present study was a two-part qualitative study (comparative review, interview with experts) on improving provision of rural primary health care.Australia, Canada, the United States, the United Kingdom, Kazakhstan, Thailand, and Iran, based on the six regional divisions of the World Health Organization, were approached with a specific approach to PHC.In-depth semistructured interviews were conducted with 25 experts in PHC.Samples were selected by purposive sampling and snowball sampling.Data analysis was performed using MAXQDA 20.Results.The results of the qualitative study after identifying the gap in the findings of the comparative study showed that four main components and fourteen sub-components (1 -evidence-based planning (transparency in decision-making, needs assessment); 2 -the role of organizations, institutions, and people (strong participation); 3 -access (access to information, geographical access); 4 -managerial approach (strengthening managerial thinking, empowering rural caregiver, management of human resources, statistics and information management, financial and payment management, equipment, physical resources management, organization, effective monitoring and supervision, empowerment of health school educators and staff experts)) were effective in improving the structure of PHC in the villages of Iran.Conclusions.With evidence-based planning, strong participation of organizations, institutions and people, access, and strengthening the management approach, one can effectively improve the structure of PHC in rural areas.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".