Bibliographic record
Abstract
This study was conducted to assess the level of satisfaction of doctors and patients from FPP, to achieve the strengths and weaknesses of the project, and to improve the weaknesses in Estahban.Considering the nature, objective of research, research questions, to review the status quo in FPP, and to assess the indices studied, descriptive study of survey type has been used.To create an overall and allinclusive plan, the satisfaction with FPP was studied from the perspectives of physicians and patients in detail.Considering the objectives of the project, two questionnaires were prepared for doctors and patients population.In this study, considering the doctors population, the census method is used that includes all medical society units.To assess the target population for patients' random method is used.After statistical analysis, the results are as follows patient satisfaction with FPP 38.3 percent higher than the average, and the lowest satisfaction is related to "satisfaction with comprehensiveness of health services needed within the center."The results of statistical analysis of the physicians' satisfaction indicates that only 23.5 percent of the physicians' satisfaction is above average, and this is while the physicians' satisfaction is lower than the minimum criteria of the assessment of this research.According to the view of doctors, the current process of health records, the amount, and quality of receiving the payment and the referral process are of the weaknesses of FPP.Statistically, there is a significant difference at 99% level between the level of satisfaction of doctors based on service (urban or rural), and between rural and urban patients there was no significant difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.948 | 0.927 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".