Population aging and physician maldistribution: A longitudinal study in Japan
Bibliographic record
Abstract
Background: Over the past two decades, population aging and the introduction of the new postgraduate medical education program in 2004 have impacted on the geographic maldistribution of physicians in Japan. The purpose of this study was to evaluate recent changes in physician distribution across municipalities from 1996 to 2012 using Gini coefficients and to clarify the impact of the new medical education program on physician distribution.Methods: We extracted the number of physicians classified by type of medical institution and municipal bodies. Gini coefficients were calculated using both population and demand for medical services. We calculated the contribution ratio (CR) of maldistribution within each type of medical institution to the whole maldistribution using Rao’s method. In addition, we calculated the incremental difference in Gini coefficients between 2002 and 2010, and calculated the CR of the incremental Gini coefficient difference for each medical institution type using Seki’s method.Results: Both Gini coefficients decreased from 1996 to 2002, and increased after 2006. The CR of other hospitals increased from 2004. The incremental difference in the Gini coefficient using demand between 2002 and 2010 was 0.012, and the CR of each type of medical institution was -25.1% (university hospitals), 131.0% (other hospitals) and -5.9% (clinics).Conclusions: Our analysis showed that the geographic maldistribution of physicians has worsened since the introduction of the new postgraduate medical education program, and the CR of maldistribution in other hospitals was high. Our study suggested that new medical resource distribution policies should be discussed to improve maldistribution.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".