PROTECTING, BALANCING, AND CONFRONTING: HEALTH-SEEKING AMONG HOMELESS YOUTH IN HO CHI MINH CITY, VIETNAM
Bibliographic record
Abstract
The objective of this study was to explore health-seeking behaviors and barriers faced in accessing care among homeless youth living in Ho Chi Minh City, Vietnam. Twelve in-depth interviews were conducted with homeless youth aged 18 to 25. Participants were identified using purposive sampling. Data were analyzed using constructivist grounded theory techniques. Interviews with youth revealed that while living on the streets, they had to balance their need for security with attending to their daily survival needs, which led to a disconnection from thinking about their health. When faced with a major health issue, youth turned to their informal networks of support instead of seeking immediate medical care. To manage their basic health needs, youth obtained medicine and health advice from local pharmacies and sought advice from social workers. Homeless youth interviewed in this study relied on an informal network of peers, social workers, and pharmacies when engaging with the health care system. They also faced several barriers to accessing health services, many of which are tied specifically to policies that make homelessness discriminated against in Vietnam. Within Vietnam’s unique political and social context, there is a need for increased collaboration between service providers such as health clinics, local pharmacies, and social workers to provide appropriate health services to this vulnerable population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".