Adapting Psychiatric Approaches to the Needs of Vulnerable Populations: A Qualitative Analysis
Bibliographic record
Abstract
Marginalized populations face significant barriers to mental health care, such as stigma, poverty, and limited access to adapted services, with conventional psychiatric approaches often falling short. This study aimed to explore how psychiatric care can be adapted to better meet the needs of vulnerable populations. Data were collected from psychiatry residents, psychiatrists, and community organization staff during a course on vulnerable populations, using semi-structured discussions and analyzed through grounded theory with iterative coding. Seven main themes emerged: (1) barriers and needs of vulnerable populations, highlighting challenges like homelessness and stigma; (2) psychiatric interventions and flexible approaches, emphasizing tailored care; (3) collaboration with community organizations, focusing on partnerships to improve care access; (4) ethical approach and respect for rights, ensuring dignity in treatment; (5) specific populations and associated challenges, addressing the needs of groups like LGBTQ+ youth and migrants; (6) intervention and support models, such as proximity-based care and post-hospitalization follow-up; (7) innovation and evolution of practices, focusing on research and institutional adaptations. This study emphasizes the need for personalized, intersectoral care, recommending improved collaboration, flexible models, and greater clinical exposure, with future research exploring how psychiatric education can better prepare clinicians to work with marginalized groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".