Using self-reported health as a social determinants of health outcome: a scoping review of reviews
Bibliographic record
Abstract
Reducing disease prevalence rather than promoting health has long been the objective of significant population health initiatives, such as the social determinants of health (SDH) framework. However, empirical evidence suggests that people with diagnosed diseases often answer the self-reported health (SRH) question positively. In pursuit of a better proxy to understand, measure and improve health, this scoping review of reviews examines the potential of SRH to be used as an outcome of interest in population health policies. Following PRISMA-ScR guidelines, it synthesizes findings from 77 review papers (published until 11 May 2022) and reports a robust association between SDH and SRH. It also investigates inconsistencies within and between reviews to reveal how variation in population health can be explained by studying the impact of contextual factors, such as cultural, social, economic and political elements, on structural determinants such as socioeconomic situation, gender and ethnicity. These insights provide informed hypotheses for deeper explorations of the role of SDH in improving SRH. The review detects several gaps in the literature. Notably, more evidence syntheses are required, in general, on the pathway from contextual elements to population SRH and, in particular, on the social determinants of adolescents' SRH. This study reports a disease-oriented mindset in collecting, analysing and reporting SRH across the included reviews. Future studies should utilize the capability of SRH in interconnecting social, psychological and biological dimensions of health to actualize its full potential as a central public health measure.
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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 | 0.027 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.024 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".