Latent profile analysis for the classification of OECD countries with health indicators
Bibliographic record
Abstract
Aims: Health indicators provide up-to-date information on the health status of a population.This study aimed to classify the Organization for Economic Co-operation and Development (OECD) countries according to health indicators and assess their status. Methods:The dataset was obtained from the OECD and World Bank databases.The most recent data from 2018 to 2022 were used.The dataset included the number of hospital beds, computed tomography scanners, magnetic resonance imaging (MRI) units, mammography machines, and radiotherapy machines as indicators of health equipment and the number of doctors, nurses, medical graduates, and nursing graduates as indicators of healthcare workers.The classification was performed using latent profile analysis (LPA).Estimated classes were compared using ANOVA or the Kruskal-Wallis test.Results: Three distinct classes were obtained from the models constructed with LPA (Akaike information criteria: 1674.91,Bayesian information criteria: 1726.87,Lo-Mendell-Rubin adjusted likelihood ratio test: p<0.001).The number of countries in the classes was 11, 14, and 4, respectively.The number of MRI units was the most prominent variable in separating the classes (p=0.001).Türkiye was in the same class as Canada, Chile, the Czech Republic, Estonia, Hungary, Israel, Luxembourg, Mexico, Poland, and Slovenia.The numbers for all indicators in Türkiye were below the average of its class, except for the numbers of MRI units and medical graduates.Conclusions: This study found the number of MRI units to be the most prominent indicator in categorizing OECD countries into three different classes, whereas the number of hospital beds and nurses did not differ across the defined classes.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".