Reach, Engagement, and Acceptability of a Subclinical Telehealth Service for Spanish-Speaking Adults: Retrospective Mixed Methods Pilot Study
Bibliographic record
Abstract
Background: There is a gap in mental health care among Latino/x and Spanish-speaking communities, and the care that is available is often difficult to access, lacks cultural nuance, and results in low engagement and satisfaction. Objective: The study aimed to evaluate the reach, adoption, and acceptability of digital Spanish-language psychosocial and emotional wellness services among Latino/x adults offered by the digital health company Sanarai. Methods: Data included in this study were collected between August 2020 and September 2024 by Sanarai as part of its ongoing services. Quantitative data sources included individual customers' appointment data, individual session payment data, and customer satisfaction data. Qualitative data were obtained from transcribed notes of telephone or video-based user interviews conducted by Sanarai staff between August 2020 and May 2024. Results: Between August 2020 and September 2024, Sanarai served 6163 users (n=3662, 59.42% women participants) across all 50 US states, with the highest concentration of participants in Texas and California. Results showed 94% (n=5793) of users scheduled a first appointment within 1 week, with 43% (n=2650) doing so within 1 day. Over 62.60% (n=3858) of participants engaged in two or more sessions, attending an average of 8.94 (SD 13) sessions over 110 days (SD 169). The platform delivered a total of 36,858 appointments, including individual and couples sessions. Only 22.47% (n=1385) of users responded to a customer satisfaction survey for a total of 2287 distinct responses; among this subgroup, session satisfaction was high with an average satisfaction rating of 4.88 out of 5.0 (SD 0.49) and a Net Promoter Score of +85. Nearly all responses (n=2174, 95.06%) expressed intent to schedule another session, but these results should be interpreted with caution, given the low response rate. Qualitative interviews with 30 users (n=21, 70% women) revealed a diverse user base. Many users reported prior mental health service experiences, while one-third were new to care. Participants cited cost, cultural fit, language access, and convenience as key reasons for choosing Sanarai over local services. Users highlighted the platform's affordability, scheduling flexibility, and provider professionalism as central to their positive experiences. Conclusions: These findings underscore the value of culturally responsive, accessible online mental health care for Spanish-speaking communities.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".