Mental health for refugees, asylum seekers and displaced persons: A call for a humanitarian agenda
Bibliographic record
Abstract
Every day, nearly 34,000 people are forcibly displaced as a result of war, conflict or persecution.Globally, more than 65 million people have been forced from their homes and about 21 million of these are refugees, over half of whom are under the age of 18.About 80% of these refugees, over 16 million, are under UNHCR (the UN Refugee Agency) mandate, and fully 5.2 million Palestinians are refugees registered by UNRWA (United Nations Relief and Works Agency for Palestine Refugees in the Near East).Approximately 10 million persons are stateless and denied access to basic rights such as education, healthcare, employment and freedom of movement.Currently, more than half of the world's refugees are from three countries: Syria, Afghanistan, and Somalia (UNHCR, 2017).The major receiving countries are Turkey, Pakistan, Lebanon, Iran, Ethiopia, and Jordan.Despite all of the popular media attention to waves of refugees, only 17% of those displaced reach Europe; 56% remain in Africa and the Middle East.Behind every number in the UNHCR statistics there is an individual story of someone forced to uproot and move due to war, conflict, persecution, and hardship-and, for many, this situation has led to persistent displacement and uncertainty in ''temporary'' situations that now span generations.We know that being an asylum seeker, refugee, or forcibly displaced has a profound impact on mental health, with an increased risk of developing common psychiatric disorders, such as, depression, anxiety, post-traumatic stress disorder (PTSD), psychotic disorders as well as disabling symptoms of psychosocial stress (Hassan, et al., 2015;2016).In addition, there is often poor access to mental health care and a lack of funding for
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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.051 | 0.055 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".