A Qualitative Study of Bottlenecks and Causes of Fractions for Dedicated Incomes of Health Centers and Solutions for their Reduction
Bibliographic record
Abstract
BACKGROUND: Primary health care is one of effective approaches for improving public health. Providing optimal cares requires supplication of various resources such as financial resources. "Fractions of incomes" in health centers is one of the remarkable problems for the domain of financial resources management in Iran. This study was aimed to identify bottlenecks and causes of fractions for incomes in health centers and solutions for their reduction. METHODS: The current study was conducted in a qualitative phenomenology method in East Azerbaijan province of Iran in 2014. Data collection method was focus group discussion and semi-structured interview. Purposive sampling was used for selecting participants. Focus group discussions and interviews were conducted based on pre-prepared guidance and continued till data saturation. Validity of guidance was approved by qualitative studies experts. Data were analyzed using content analysis method. RESULTS: Based on the opinions of participants, two and six themes were respectively extracted for bottlenecks of fractions and causes and solutions for their reduction. Themes for bottlenecks of fractions included cash (monetary) and non-cash (non-monetary) fractions and themes for causes and solutions included causes and solutions for fractions per capita, insurance deductions, fractions related to sending documents, registration fractions, discounts fractions, and incomplete deposit of cash incomes. CONCLUSION: All cash and non-cash incomes of health centers are subject to fractions. The causes of fractions are related to the whole process of converting services to incomes and insurance requirements. Identified solutions and interventions also focus on these areas.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".