Guest Editors' Introduction: In Pursuit of the Social Determinants of Health: The Evolution of Health Services Research
Bibliographic record
Abstract
The Evolution of Health Services ResearchHealth services research typically concerns itself with issues of organization, financing, utilization, and costs of health care.Improving access to and the delivery of high-quality, efficient care, it is hoped, will improve the health of the population.Over the past several decades, researchers have increasingly documented that the portion of population health status attributable to medical care is modest, when compared with the contributions of other factors, including health behaviors, psychosocial and environmental factors, and genetic endowment (McGinnis and Foege 1993).Indeed, Healthy People 2010, a federally led effort that outlines the nation's public health objectives for the current decade, identified access to health care as only one of ten leading health indicators that, in addition to income and education, could serve as bellwethers for the health of the population, much the way the leading economic indicators forecast the health of the nation's economy (Department of Health and Human Services 2001).The other nine indicators comprise the combination of modifiable behavioral, social, and environmental factors known to affect health.This reflects the development of a body of research and theory on the social determinants of population health, which has in other nations such as Canada and the United Kingdom begun to influence the making of social policy broadly related to health (Acheson 1998).In developing this special issue of Health Services Research, we hoped to highlight for health services researchers the importance of factors that contribute to health beyond the health care delivery system, and to identify some promising policy directions for improving health.The submissions we received, and those that appear in this issue, reflect both the strengths and gaps in our field.We were gratified that submissions concerned the entire life cycle, from birth through old age; reflected a broad conceptualization of health, including mental health and oral health; and considered a wideranging list of potential determinants of health, including not only health care but also income and income distribution, racial/ethnic segregation, and discrimination, among others.They reflected some of the major strengths of our field, In Pursuit of the Social Determinants of Health 1643
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".