Integrated Care as a Solution to Address Complexities in Logistical Access and Mobility: A Community-Based Study on Social Equity in Health
Bibliographic record
Abstract
Building upon existing literature on physical barriers experienced by higher-needs patients and marginalized communities, this paper presents a targeted analysis of qualitative data under a parent study on healthy cities. To optimize equity in access to health care as one of the many life opportunities affected by transportation, semi-structured interviews were conducted with community members reflecting diverse statuses of marginalization in addition to both mental and physical health challenges (n = 17), supplemented in dialogue with global policymakers (n = 5). Participant responses reveal how transportation time, costs, and risk strongly determine the actual confines of the world where they experience sufficient autonomy to access life opportunities. How these daily opportunities intersect with research on food deserts, education deserts, childcare deserts, and health care deserts indicates that physical barriers can emerge as a baseline consideration affecting ongoing experiences of socio-structural determinants. Most notably, the act of accessing care in itself, when involving transportation, can also present its own health risks requiring further integrated care services – given the body of evidence on injury, crime-related mental health stressors for marginalized groups, in addition to longitudinal public health concerns related to cardiovascular health and climate change risks. Access to both mental health care and physical health care can both be linked to urban development, intersecting with socioeconomic positionality amidst urban-rural environments – as physical access emerges with significant intersections with social work that is sensitive to the housing and employment needs of families. A four–part framework for integrated health care toward equitable outcomes in social work concludes the study.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".