“I don’t think they’re as culturally sensitive”: a mixed-method study exploring e-mental health use among culturally diverse populations
Bibliographic record
Abstract
BACKGROUND: Culturally diverse populations (CDPs), such as visible minorities, face challenges, such as lack of culturally tailored resources, when accessing mental health services. These barriers may be addressed by e-mental health (eMH) technologies. However, little attention has been devoted to understanding the cultural responsiveness of these services among CDPs. AIMS: This study explores CDPs experience of eMH for anxiety and depressive disorders in an urban area and gauge its cultural responsiveness. METHODS: = 14) shared their experiences through semi-structured focus groups. RESULTS: The majority of participants (68%) indicated that the eMH resources used were not culturally tailored. However, most participants (65%) agreed that the resource was available in their preferred language. Focus group discussions revealed key experiences around limited language diversity, cultural representation and cultural competency, and culturally linked stigma. eMH recommendations suggested by participants focused on including culturally tailored content, graphics and phrases, and lived experiences of CDPs. CONCLUSIONS: The findings showcase the need for more culturally responsive eMH beyond language translation, while providing healthcare professionals with a greater and nuanced understanding of treatment needs in cultural groups.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".