HOW ARE THE FORMAL AND INFORMAL ONLINE SUPPORTS OF MENTAL HEALTH ACCESSIBLE FOR REFUGEES AND THEIR CHILDREN IN CANADA?
Bibliographic record
Abstract
Refugees fleeing from extreme human rights violations are highly vulnerable and predisposed to a variety of mental health illnesses. The issue that this study addresses are the barriers refugees encounter when navigating mental health resources in Canada. Across the literature it has been found that refugees tend to underutilize mental health resources for a variety of reasons despite their poor mental health outcomes. Some factors of underutilization include, linguistic, religious, cultural, and economic (Chaze et al., 2015). To address this problem, the purpose of this study will be to evaluate the online accessibility of available resources. A content analysis on the Ontario and British Columbia settlement websites was conducted. This paper will address the following research questions: (1) What are the formal and informal support channels for the mental health of refugees? (2) What services are provided by these supports to serve the mental health of refugees? and (3) What are the online barriers refugees may face when navigating these websites? This data led to the following themes: Government versus NGOs, Type of Services Offered, and Online Barriers. These themes are useful to understand the gaps in the literature that indicate refugees underutilize mental health resources. Furthermore, it will provide insight as to why refugees may undergo hardship when navigating mental health websites.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".