Exploring cultural responsiveness of e-mental health resources for depressive and anxiety disorders
Bibliographic record
Abstract
Background: Canada’s culturally diverse populations (CDPs) experience difficulties such as language barriers, difficulty navigating the healthcare system, and lack of culturally tailored resources compared to the general population when accessing mental health services. These surmountable barriers may be addressed by e-mental health (eMH) technologies that allow for mental healthcare to be delivered through the Internet and related technologies. However, little attention has been devoted to understanding the cultural responsiveness of these services among CDPs. Objectives: This study investigates the use of eMH among CDPs for anxiety and depressive disorders in an urban area. Our objectives are to (1) explore the experience of eMH services and gauge their cultural responsiveness, (2) examine participants’ digital health literacy, mental health status, and usage of eMH; and (3) develop recommendations based on participants’ experiences to improve eMH services. Methods: Participants (N=136) completed a survey regarding their eMH use, the severity of their depression and anxiety symptoms, and socio-demographic characteristics. Participants (N=14) shared experiences through semi-structured focus group discussions. From this, we developed a set of guidelines based on the experiences and recommendations from participants for future eMH resources. Participants (N=5) were invited to provide feedback through one-on-one interviews. Results: Survey participants’ ages ranged from 19 to 74 years, with 43% within young adult ages of 19 to 24. Of these participants, 65% were women, 22% were men, while 3% identified as Trans Male, Non-Binary, or Other. Most survey participants identified as South Asian (40%) or Chinese (28%). The majority of participants (68%) indicated that the eMH resources they used, overall, were not culturally tailored. However, most participants (65%) agreed that the resource was available in their preferred language. Focus group discussions revealed themes of facilitators and barriers of help-seeking behaviours and sociocultural contexts. eMH recommendations suggested by participants’ responses focused on including culturally tailored content, graphics and phrases, and lived experiences of CDPs while reducing culturally linked stigma. Conclusion: 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.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".