Analysis of Developmental Level of Counties of Fars in Terms of Health Infrastructure Indicators Using Standardized Scores Pattern Approach and Factor Analysis
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: It is necessary for planning in order to achieve optimal development, to have knowledge and understanding of the current situation. This identification requires separate areas of study into planning and assessing regions of each area with development indicators and analysis and ranking each area in terms of having gifts of development. The study also aims to analyze the development level of counties of Fars in terms of health infrastructure indicators using standardized scores pattern and factor analysis. METHODS: This is a descriptive and applied study, which has discussed the levels of 29 counties of Fars based on 10 health selected indicators using a standardized scoring model. Data were collected using a data collection form developed by the researchers through the Center of Statistics and Shiraz University of Medical Sciences. Results were analyzed using Excel and SPSS 19. RESULTS: Based on calculations according to standardized score and factor analysis methods, Shiraz and Rostam had the most and the least level between the other cities, respectively. Also development coefficient and operating score of the studied counties ranged from a maximum of 0.894 to a minimum of -0.941, and a maximum of 3.861 to a minimum of 2.001, respectively. DISCUSSION: There are relatively large differences between different counties in healthcare sector, and most studied counties in terms of healthcare sector indicators are not satisfactory. So planning how to allocate healthcare resources from policy makers to improve the studied counties is essential.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it