Healthcare needs and health service utilization by Syrian refugee women in Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Access to healthcare is an important part of the (re)settlement process for Syrian refugees in Canada. There is growing concern about the healthcare needs of the 54,560 Syrian refugees who were admitted to Canada by May 2018, 80% of whom are women and children. We explored the healthcare needs of newcomer Syrian women, their experiences in accessing and using health services, and the factors and conditions that shape whether and how they access and utilize health services in the Greater Toronto Area (GTA). METHOD: This community-based qualitative descriptive interpretive study was informed by Yang & Hwang (2016) health service utilization framework. Focus group discussions were held with 58 Syrian newcomer women in the GTA. These discussions were conducted in Arabic, audio-recorded with participants' consent, translated into English and transcribed, and analyzed using thematic analysis. RESULTS: Participants' health concerns included chronic, long-term conditions as well as new and emerging issues. Initial health insurance and coverage were enabling factors to access to services, while language and social disconnection were barriers. Other factors, such as beliefs about naturopathic medicine, settlement in suburban areas with limited public transportation, and lack of linguistically, culturally, and gender-appropriate services negatively affected access to and use of healthcare services. CONCLUSION: Responding to the healthcare needs of Syrian newcomer women in a timely and comprehensive manner requires coordinated, multi-sector initiatives that can address the financial, social, and structural barriers to their access and use of 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.
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 | 0.001 | 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 it