A Conceptual Framework to Design Connected Mental Health Solutions in the United Arab Emirates: Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Connected mental health (CMH) is a field presenting information and communications technology-based mental care interventions that could help overcome many mental care delivery barriers. Culture and background influence people's attitudes, preferences, and acceptance of such solutions. Therefore, the suitability of CMH solutions to the targeted population is an important factor in their successful adoption. OBJECTIVE: The aim of this study is to develop a framework for the design and creation of CMH solutions suitable for the UAE context. The framework is based on investigating enablers and barriers of CMH adoption in the United Arab Emirates, from the mental health professional's (MHP) perspective and from related literature. METHODS: A survey of literature on relevant studies addressing the use of technology for mental care in Arab countries, and a web-based questionnaire-based survey with 17 MHPs practicing in the United Arab Emirates investigating their attitudes and views toward CMH was conducted. Results from the questionnaire and from related studies were analyzed to develop the design framework. RESULTS: On the basis of findings from the literature survey and analyzing MHP answers to the web-based survey, a framework for the design of CMH solutions for the UAE population was developed. The framework presents four types of recommendation categories: favorable criteria, which included blended care, anonymity, and ease of use; cultural factors including availability in multiple languages, mainly Arabic and English, in addition to religious and cultural considerations; technical considerations, including good-quality communication, availability in formats compatible with mobile phones, and providing technical support; and users' health and data safety considerations, including users' suitability testing, confidentiality, and ensuring MHP integrity. CONCLUSIONS: CMH has the potential to help overcome many mental care barriers in the United Arab Emirates in particular and in the Arab world in general. CMH adoption in the United Arab Emirates has a potential for success. However, many factors should be taken into account, mainly cultural, religious, and linguistic aspects.
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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 | 0.030 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".