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Record W4404402064 · doi:10.2196/63298

Cultural Adaptation and User Satisfaction of an Internet-Delivered Cognitive Behavioral Program for Depression and Anxiety Among College Students in Two Latin American Countries: Focus Group Study With Potential Users and a Cross-Sectional Questionnaire Study With Actual Users

2024· article· en· W4404402064 on OpenAlexvenueno aff
Yesica Albor, Noé González, Corina Benjet, Alicia Salamanca-Sanabria, Cristiny Hernández-de la Rosa, Viridiana Eslava-Torres, María Carolina García-Alfaro, Andrés Melchor-Audirac, Laura Itzel Montoya-Montero, Karla Suárez

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersFogarty International CenterNational Institute of Mental Health
KeywordsPsychological interventionPsychologyFocus groupMental healthAnxietyApplied psychologyPopulationThe InternetAdaptation (eye)Medical educationClinical psychologyMedicinePsychiatryComputer scienceSociology

Abstract

fetched live from OpenAlex

BACKGROUND: To scale up mental health care in low-resource settings, digital interventions must consider cultural fit. Despite the findings that culturally adapted digital interventions have greater effectiveness, there is a lack of empirical evidence of interventions that have been culturally adapted or their adaptation documented. OBJECTIVE: This study aimed to document the cultural adaptation of the SilverCloud Health Space from Depression and Anxiety program for university students in Colombia and Mexico and evaluate user satisfaction with the adapted program. METHODS: A mixed methods process was based on Cultural Sensitivity and Ecological Validity frameworks. In phase 1, the research team added culturally relevant content (eg, expressions, personal stories, photos) for the target population to the intervention. In phase 2, potential users (9 university students) first evaluated the vignettes and photos used throughout the program. We calculated median and modal responses. They then participated in focus groups to evaluate and assess the cultural appropriateness of the materials. Their comments were coded into the 8 dimensions of the Ecological Validity Framework. Phase 3 consisted of choosing the vignettes most highly rated by the potential users and making modifications to the materials based on the student feedback. In the final phase, 765 actual users then engaged with the culturally adapted program and rated their satisfaction with the program. We calculated the percentage of users who agreed or strongly agreed that the modules were interesting, relevant, useful, and helped them attain their goals. RESULTS: The potential users perceived the original vignettes as moderately genuine, or true, which were given median scores between 2.5 and 3 (out of a possible 4) and somewhat identified with the situations presented in the vignettes given median scores between 1.5 and 3. The majority of comments or suggestions for modification concerned language (126/218, 57.5%), followed by concepts (50/218, 22.8%). Much less concerned methods (22/218, 10%), persons (9/218, 4.1%), context (5/218, 2.3%), or content (2/218, 0.9%). There were no comments about metaphors or goals. Intervention materials were modified based on these results. Of the actual users who engaged with the adapted version of the program, 87.7%-96.2% of them agreed or strongly agreed that the modules were interesting, relevant, useful, and helped them to attain their goals. CONCLUSIONS: We conclude that the adapted version is satisfactory for this population based on the focus group discussions and the satisfaction scores. Conducting and documenting such cultural adaptations and involving the users in the cultural adaptation process will likely improve the effectiveness of digital mental health interventions in low- and middle-income countries and culturally diverse contexts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.054
GPT teacher head0.495
Teacher spread0.441 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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