Community Health Workers as Mental Health Paraprofessionals: Protocol for a Mixed-Methods Pilot Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Community health workers (CHWs) are effective in delivering behavioral activation (BA), especially in low-resource settings. In an area with a lack of Spanish-speaking mental health counselors, such as southwest Montana, CHWs can provide needed care. OBJECTIVE: The goal of this pilot study protocol is to test the feasibility, acceptability, and preliminary efficacy of a model of care that engages CHWs as providers of BA. METHODS: We will train 2 CHWs in BA methodology. We will enroll 20 participants who screen positive for depression in a 12-week telephone intervention for BA. Preliminary efficacy will be tested in pre- and postscores of the Beck Depression Inventory and semistructured interviews. Feasibility and acceptability will be measured through participant retention and treatment adherence. The Therapeutic Alliance with Clinician Scale will be used to measure the strength of the therapeutic relationship. Descriptive statistics will measure alliances and repeated measures ANOVA will measure trends and changes in depression scores. RESULTS: Enrollment began in October 2023. A total of 12 participants completed at least 10 BA sessions and all study measures by the time the study concluded in May 2024. In August 2024, data analysis occurred with an anticipated manuscript to be submitted for publication in October 2024. CONCLUSIONS: Results from this study will inform future studies into the implementation of an evidence-based mental health intervention in a limited resource setting for Latino people with limited English proficiency. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57343.
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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 | 0.071 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.085 | 0.023 |
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".