Variation matters and should be included in health care research for comparison of outcomes
Bibliographic record
Abstract
BACKGROUND: Health care is provided under the conditions in which people live and under the rules and regulations of a prevailing health system. As a consequence, 'local' circumstances are an important determinant of the actual care that can be provided and its effects on the health of individuals and populations. This plays in particular, but not exclusively, a role in community-based primary health care. Although this is generally accepted, there is little insight in the impact of the setting and context in which health care is provided on the outcome of care. Aim This paper argues the case to use this natural variation within and between countries as an opportunity to be used as a form of natural experiment in health research. Arguments We argue that analysing and comparing outcomes across settings, that is comparative outcomes of interventions that have been performed under different health care conditions will improve the understanding of how the real-life setting in which health care is provided - including the health system, the socio-economic circumstances and prevailing cultural values - do determine outcome of care. Recommendations To facilitate comparison of research findings across health systems and different socio-economic and cultural contexts, we recommend a more detailed reporting of the conditions and circumstances under which health research has been performed. A set of core variables is proposed for studies in primary health care.
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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 | 0.509 | 0.797 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".