Adult newcomers’ perceptions of access to care and differences in health systems after relocation from Syria
Bibliographic record
Abstract
BACKGROUND: In Canada, approximately 13% of the population lives with multiple chronic conditions. Newcomers, including refugees, have the same or higher risk of developing chronic diseases as their host population. In 2015-2016, Canada welcomed almost 40, 000 newcomers from Syria. This study aimed to (1) understand adult newcomer health needs for self-management of non-infectious chronic conditions; and (2) identify strategies to improve access to health care services to meet these needs. METHODS: This study used a qualitative descriptive design. Interviews and focus groups were conducted with consenting newcomers, service providers and community agency administrators. Interview guides were developed with input from community partners and snowball sampling was used. RESULTS: Participants included 22 Syrian newcomers and 8 service providers/administrators. Findings revealed the initial year of arrival as one of multiple adjustments, often rendering chronic disease management to a lower priority. Self-care and self-management were not routinely incorporated into newcomer lives though community health agencies were proactive in creating opportunities to learn self-management practices. Gaps in access to care were prevalent, including mental health services which typically were not well developed for trauma and post-traumatic stress disorder (PTSD), particularly for men. Newcomers expressed frustration with lengthy wait times and not being able to access specialists directly. Youth frequently played a key role in translation and disseminating information about services to their families. CONCLUSION: Chronic disease management was a low priority for newcomers who were focussed on resettlement issues such as learning English or finding work. Provision of practical supports such as bus tickets, translation, and information about the healthcare system were identified as means of improving access to care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".