Mental health among older Bhutanese with a refugee life experience: A mixed-methods latent class analysis study.
Bibliographic record
Abstract
There are disparities in the mental health of refugee populations compared to individuals who have not experienced forced migration. It is important to identify individuals with a refugee life experience who are most in need of mental health care and prioritize their engagement in services. The objectives of this convergent mixed-methods study are to quantitatively identify the association between exposure to pre- and postresettlement traumas and stressors and mental health among older adults with a refugee life experience, qualitatively identify typologies of narratives of forced migration, and integrate findings to provide a more comprehensive understanding of the relationship between trauma and symptoms of posttraumatic stress disorder (PTSD). Study participants were Bhutanese with a refugee life experience living in a metropolitan area in New England (United States). We used quantitative surveys to identify exposures to traumas and symptoms of PTSD. We used latent class analysis to identify subgroups of trauma exposure and association with symptoms of PTSD. A subset of individuals participated in qualitative interviews. Narrative thematic analysis was used to explore typologies of life history narratives. Quantitatively, we identified four classes of patterns of trauma exposure throughout the refugee life trajectory. These classes were associated with current symptoms of PTSD. Qualitatively, we identified four narrative types that indicate participants interpreted and made sense of their life trajectories in a variety of ways. Integration of findings indicate that caution is needed in identifying individuals in need of mental health services and the best approach for interventions that promote psychosocial well-being. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".