MENTAL HEALTH CONCEPTUALIZATION, COPING, AND HELP-SEEKING BEHAVIOUR AMONG ARABIC-SPEAKING REFUGEES IN HAMILTON, ONTARIO: A QUALITATIVE STUDY
Bibliographic record
Abstract
Background: The Syrian refugee crisis is one of the most significant humanitarian crises of our time and has resulted in over 6.5 million displaced individuals worldwide. Syrian refugees are a vulnerable population and are at considerably higher risk for mental health disorders including depression, anxiety, and post-traumatic stress disorder. However, despite the high prevalence, there is insufficient utilization of mental health services among Syrian refugees resettled in high-income countries. To help address this gap this study aims to investigate mental health conceptualization, coping, and help-seeking among Syrian refugee parents resettled in Canada to build a comprehensive understanding of the factors influencing perception and the decisions to seek help, thus adding to the knowledge base for refugee mental health and generating insight to help inform policy and program decisions for Syrian refugees resettled in Canada. Methods: Data was collected using semi-structured interviews with Syrian refugee parents (N=31) who have been permanently resettled in Canada. Interviews were conducted in Arabic and transcribed verbatim and were subsequently translated into English. Thematic analysis was used to analyze the data. Results Significant interlinkages were observed between the factors that influence mental health conceptualization, coping, and help-seeking. Our findings suggest that many refugees perceive mental health concerns as part of daily life and do not believe it requires professional intervention. This along with personal, cultural, and religious context have strong implications for help-seeking behaviour. Moreover, the availability of culturally sensitive services has the potential to increase service utilization. Knowledge of how individuals conceptualize mental health and cope can be leveraged to design more impactful mental health services for Syrian refugees. Conclusion: The factors influencing mental health conceptualization, coping, and help-seeking are deeply interconnected and must be considered holistically to improve policies and programming to increase the uptake of mental health services.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".