Inequality in provision of medical care in Sweden: a case of social epidemiological hypochondria?
Bibliographic record
Abstract
Socioeconomic disparities in health arise through multiple pathways, including differential exposure,1 potentiation of exposure effects by comorbidities or other vulnerabilities,2 differential access to medical intervention,3 and lower quality or intensity of medical care. The last of these is the focus of the methodologically impressive new study by Ohlsson and colleagues in this issue of JECH ( see page 678 ).4 The authors use a remarkably comprehensive data resource, which captures 13% of Sweden's entire population in a multi-level, linked-record database that includes five decades of population surveillance.5 Using linkage to the Swedish Prescribed Drug Register, the authors were able to track all residents of the Skane region of Sweden who received a statin prescription from a physician during a 6-month period in 2005. They investigated whether use of recommended treatment was associated with patients' social characteristics, such as marital status and income, or with clinic characteristics, such as percentage of high-income patients seen at the facility. The authors conducted the statistical analysis using sophisticated hierarchical models that not only accounted properly for the clustered nature of the data (ie, for unmeasured similarities among patients within a clinic), but which also allowed for explicit decomposition of the variance into between-clinic/area and within-clinic/area …
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".