An analytical framework for assessing data for health services research
Bibliographic record
Abstract
Abstract This presentation will provide an overview of the conceptual framework we used as a basis for the analysis of the case studies. The framework distinguishes between data sets with care-relevant data i) at the individual level (i.e. microdata) and ii) at the non-individual level, and iii) from three or four large content blocks (health data, health care data, socio-demographic or economic data, environmental data). Using health data as an example, individual-level health data includes individual patient data, such as laboratory and clinical results, vital signs (body temperature, pulse rate, and respiration date), as well as diagnoses and health behavior. Non-individual level data includes aggregated data in areas such as life expectancy, years of life lost (YLL), years lost to disability (YLD), disability-adjusted life years (DALY), as well as population characteristics such as prevalence of risk factors and chronic illness. The framework we have developed shows linking possibilities that are available by either storing the data in common databases (e.g., based on an electronic health record) or by linking them via a unique personal characteristic (e.g., patient identifier). The country case studies selected in the research - largely within the European region but also Australia, Canada, the Republic of Korea, New Zealand, and the United States - are all evaluated using the same conceptual framework.
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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 | 0.259 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".