Social and Structural Drivers of Health and Transition to Adult Care
Bibliographic record
Abstract
CONTEXT: Youth with chronic health conditions experience challenges during their transition to adult care. Those with marginalized identities likely experience further disparities in care as they navigate structural barriers throughout transition. OBJECTIVES: This scoping review aims to identify the social and structural drivers of health (SSDOH) associated with outcomes for youth transitioning to adult care, particularly those who experience structural marginalization, including Black, Indigenous, and 2-spirit, lesbian, gay, bisexual, transgender, queer or questioning, and others youth. DATA SOURCES: Medline, Embase, CINAHL, and PsycINFO were searched from earliest available date to May 2022. STUDY SELECTION: Two reviewers screened titles and abstracts, followed by full-text. Disagreements were resolved by a third reviewer. Primary research studying the association between SSDOH and transition outcomes were included. DATA EXTRACTION: SSDOH were subcategorized as social drivers, structural drivers, and demographic characteristics. Transition outcomes were classified into themes. Associations between SSDOH and outcomes were assessed according to their statistical significance and were categorized into significant (P < .05), nonsignificant (P > .05), and unclear significance. RESULTS: 101 studies were included, identifying 12 social drivers (childhood environment, income, education, employment, health literacy, insurance, geographic location, language, immigration, food security, psychosocial stressors, and stigma) and 5 demographic characteristics (race and ethnicity, gender, illness type, illness severity, and comorbidity). No structural drivers were studied. Gender was significantly associated with communication, quality of life, transfer satisfaction, transfer completion, and transfer timing, and race and ethnicity with appointment keeping and transfer completion. LIMITATIONS: Studies were heterogeneous and a meta-analysis was not possible. CONCLUSIONS: Gender and race and ethnicity are associated with inequities in transition outcomes. Understanding these associations is crucial in informing transition interventions and mitigating health inequities.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".