Opportunities of integrated care to improve equity for adults with complex needs: a qualitative study of case management in primary care
Bibliographic record
Abstract
BACKGROUND: People living in precarious socio-economic conditions are at greater risk of developing mental and physical health disorders, and of having complex needs. This places them at risk of health inequity. Addressing social determinants of health (SDH) can contribute to reducing this inequity. Case management in primary care is an integrated care approach which could be an opportunity to better address SDH. The aim of this study is to better understand how case management in primary care may address the SDH of people with complex needs. METHODS: A case management program (CMP) for people with complex needs was implemented in four urban primary care clinics. A qualitative study was conducted with semi-structured interviews and a focus group with key informants (n = 24). An inductive thematic analysis was carried out to identify emerging themes. RESULTS: Primary care case managers were well-positioned to provide a holistic evaluation of the person's situation, to develop trust with them, and to act as their advocates. These actions helped case managers to better address individuals' unmet social needs (e.g., poor housing, social isolation, difficulty affording transportation, food, medication, etc.). Creating partnerships with the community (e.g., streetworkers) improved the capacity in assisting people with housing relocation, access to transportation, and access to care. Assuming people provide their consent, involving a significant relative or member of their community in an individualized services plan could support people in addressing their social needs. CONCLUSIONS: Case management in primary care may better address SDH and improve health equity by developing a trusting relationship with people with complex needs, improving interdisciplinary and intersectoral collaboration and social support. Future research should explore ways to enhance partnerships between primary care and community organizations.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".