Immigrant Wellbeing Project Study Protocol: Addressing the Socio-Structural Determinants of Latinx Immigrant Mental Health
Bibliographic record
Abstract
(IWP) study is to test a transdisciplinary ecological approach to reducing Latinx immigrants' mental health disparities by adapting and integrating a multilevel community-based advocacy, learning, and social support intervention into existing efforts at four community partner organizations that focus on mental health, education, legal issues, and community mobilization for Spanish-speaking immigrants. This protocol paper describes a study designed to advance the science of multilevel interventions and health equity through the conceptualization of the IWP intervention model as one component of complex processes and interventions that aim to create sustainable change at multiple levels. After completing in-depth qualitative interviews with 24 Latinx immigrants to elucidate their mental health needs, stressors, political/economic/social context, and local solutions, and a process of community engagement and intervention adaptation, a mixed methods strategy with data collected from 60 participants at four timepoints over 12 months will be used to test the impact of the 6-month intervention on reducing psychological distress, increasing protective factors, and achieving system-level changes in policies and practices that impact Latinx immigrants' well-being. Mechanisms of change will be explored by testing mediating relationships between protective factors and distress. Qualitative data will explore feasibility, acceptability, and participants' experiences; document multilevel changes and the context of implementation; and inform interpretation of quantitative data. Data on quality of community partnerships and their relationship to multilevel outcomes will also be examined. This paper describes the theoretical foundation, research design, qualitative and quantitative analysis plans, and innovations of the study.
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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 | 0.031 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".