Digital Mental Health Resources for Asylum Seekers, Refugees, and Immigrants: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Asylum seekers, refugees, and immigrants experience a number of risk factors for mental health problems. However, in comparison to the host population, these populations are less likely to use mental health services. Digital mental health approaches have been shown to be effective in improving well-being for the general population. Thus, they may provide an effective and culturally appropriate strategy to bridge the treatment gap for these populations vulnerable to mental health risks. OBJECTIVE: This paper aims to provide the background and rationale for conducting a scoping review on digital mental health resources for asylum seekers, refugees, and immigrants. It also provides an outline of the methods and analyses, which will be used to answer the following questions. What are the available digital mental health resources for asylum seekers, refugees, and immigrants? Are they effective, feasible, appropriate, and accepted by the population? What are the knowledge gaps in the field? METHODS: The scoping review methodology will follow 5 phases: identifying the research question; identifying relevant studies; study selection; charting the data; and collating, summarizing, and reporting the results. Searches will be conducted in the following databases: EBSCOhost databases (CINAHL Plus with Full Text, MEDLINE with Full Text, APA PsycArticles, Psychology and Behavioral Sciences Collection, and APA PsycInfo), PubMed, and Scopus. Additionally, OpenGrey, Mednar, and Eldis will be searched for gray literature. All primary studies and gray literature in English concerning the use of information and communication technology to deliver services addressing mental health issues for asylum seekers, refugees, and immigrants will be included. RESULTS: This scoping review will provide an overview of the available digital mental health resources for asylum seekers, refugees, and immigrants and describe the implementation outcomes of feasibility, acceptability, and appropriateness of such approaches for those populations. Potential gaps in the field will also be identified. CONCLUSIONS: As of February 2020, there were no scoping reviews, which assessed the effectiveness, feasibility, acceptability, and appropriateness of the available digital mental health resources for asylum seekers, refugees, and immigrants. This review will provide an extensive coverage on a promising and innovative intervention for such populations. It will give insight into the range of approaches, their effectiveness, and progress in their implementation. It will also provide valuable information for health practitioners, policy makers, and researchers working with the population. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/19031.
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 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 | 0.092 | 0.074 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.080 | 0.014 |
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".