Acceptability of the Urban Family Medicine Project among Physicians: A Cross-Sectional Study of Medical Offices, Iran
Bibliographic record
Abstract
INTRODUCTION: It is essential to organize private physicians in urban areas by developing urban family medicine in Iran. Acceptance of this project is currently low among physicians. The present research determined the factors affecting acceptability of the Urban Family Medicine Project among physicians working in the private sector of Mazandaran and Fars provinces in Iran. METHODS: This descriptive-analytical and cross-sectional study was conducted in Mazandaran and Fars provinces. The target population was all physicians working in private offices in these regions. The sample size was calculated to be 860. The instrument contained 70 items that were modified in accordance with feedback from eight healthcare managers and a pilot sample of 50 physicians. Data was analyzed using the LISREL 8.80. RESULTS: The response rate was 82.21% and acceptability was almost 50% for all domains. The fit indices of the structural model were the chi-square to degree-of-freedom (2.79), normalized fit index (0.98), non-normalized fit index (0.99), comparative fit index (0.99), and root mean square error of approximation (0.05). Training facilities had no significant direct effect on acceptability; however, workload had a direct negative effect on acceptability. Other factors had direct positive effects on acceptability. CONCLUSION: Specification of the factors relating to acceptance of the project among private physicians is required to develop the project in urban areas. It is essential to upgrade the payment system, remedy cultural barriers, decrease the workload, improve the scope of practice and working conditions, and improve collaboration between healthcare professionals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".