Assessment the Trend of Inequality in the Distribution of Intensive Care Beds in Iran: Using GINI Index
Bibliographic record
Abstract
BACKGROUND & AIM: While most of the published researches have reported the amount of inequity in geographical distribution of important health resources, only a small number of studies have focused on the trend of inequality in the distribution of these resources. The purpose of this study was to determine the trend of inequality in the distribution of intensive care beds in Iran during 2010 to 2012 by using the Gini coefficient. METHODS: This is a cross-sectional research conducted in 2013. The changes over three years (2010 to 2012) were calculated by Gini coefficient to investigate the trend of inequality in geographical distribution of intensive care beds (CCU, ICU and NICU). RESULTS: The Gini coefficient for CCU beds was calculated as 0.02, 0.04 and 0.06 in 2010, 2011 and 2012, respectively. The Gini coefficient for ICU beds was calculated as 0.03, 0.05 and 0.05 in 2010, 2011 and 2012, respectively. Also, the Gini coefficient for NICU bed was calculated as 0.02, 0.03 and 0.04 in 2010, 2011 and 2012, respectively. CONCLUSION: Regarding to Gini coefficient, the trend of inequality was increased in the distribution of intensive care beds in Iran. Particularly, the inequalities in distribution of CCU beds were significantly increased during past years. In fact, if this trend of inequality continues, the distribution of intensive care beds will be extremely unequal in the next five years in Iran.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".