Supporting the mental health and wellbeing of Syrian adults with refugee experiences: Considerations for the nurse practitioner in Canada
Bibliographic record
Abstract
Synthesizing the findings from diverse methodologies, this integrative review aimed to guide nurse practitioners (NPs) in supporting the mental health and wellbeing of Syrian adults with refugee experiences in Canada (Syrian Adults). A comprehensive search strategy was conducted in three databases (APA PsycInfo, MEDLINE with Full Text (EBSCO) and Web of Science), Google Scholar and Google Search Engine to identify relevant literature. Twelve publications met the inclusion criteria and were critically appraised. The analysis of these publications led to the identification of four key themes: (1) Correlates and Social Determinants of Mental Health, (2) Cultural and Linguistic Considerations (including the subthemes of Stigma and Perceptions of Mental Distress; and Screening and Self-Report Tools), (3) Non-Clinical Facilitators of Mental Wellbeing, and (4) Trauma and Provision of Trauma-Informed Care. The findings of this integrative review highlight the importance for NPs to build their foundational knowledge to provide comprehensive, culturally appropriate, and trauma-informed care. Key strategies include the use of validated mental health screening tools tailored for Arabic-speaking and/or refugee populations, fostering trust and safety through an awareness of the role of stigma and trauma in mental health care, utilizing interpreters and cultural brokers where appropriate, and assessing social determinants of health during routine assessments. This review also emphasizes the importance of interagency collaboration and ongoing professional development in cross-cultural mental health to support access to care and mental wellbeing for Syrian Adults.,
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".