Barriers and potential solutions to mental healthcare access for youth refugees and asylum seekers
Bibliographic record
Abstract
Globally, over 82.4 million people were forcibly displaced in 2020, about 42% (35 million) of which are children and youth. Youth, aged 15 to 24, are highly susceptible to mental health difficulties, particularly those who are refugees and asylum seekers. Serious post-traumatic stress disorder, depression, and anxiety symptoms have been seen in youth refugees and asylum seekers months after they have resettled in their host countries. Yet, they encounter numerous barriers to accessing mental health support. This infographic illustrates the preliminary findings of an integrative review conducted to determine the barriers to mental health access of youth refugees and asylum seekers as well as the potential solutions to these barriers. CINAHL, PubMed, PsycINFO, EMBASE, Web of Science, ProQuest Dissertations & Theses Global, and other relevant organizations’ websites were searched for published and unpublished articles. Data from eligible articles were extracted and analyzed through thematic analysis. Findings from this review have the potential to inform future research, policy, and practice.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".