Public Health Research on Severe Mental Conditions Among Immigrant Communities in the United States: Strategies From a Qualitative Study with South Asian Immigrants in New York City
Bibliographic record
Abstract
Introduction: The study of severe mental conditions has primarily remained under the purview of basic and clinical research. Although global epidemiological data indicate that immigrant groups are at higher risk of these conditions, U.S. data are lacking. Qualitative studies can be an important first step to bring attention to understudied phenomena. Methods: =4) in New York City. These strategies were synthesized from the team's internal notes of adaptations during the study design and data collection, weekly debrief meetings during data analysis, and brainstorm sessions for this manuscript. Results: The main results of the study are reported elsewhere. This section focuses on lessons learned to improve immigrant participant interest and engagement, including the strengths and limitations of the healthcare setting; recruitment by a multilingual South Asian psychiatrist; interviews by non-clinical South Asian researchers selected for a variety of ages, genders, and languages; and the interview process and content. Discussion: Overall, these strategies show the feasibility of non-clinical researchers to collect high-quality data about severe mental conditions among immigrant communities, noting that the details of specific strategies and results will be particular to each immigrant community. Public health research on severe mental conditions is essential to understand and address the experiences of severe mental conditions among immigrant communities in the U.S.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".