73 Parenting Experiences of Families of Refugee Background in the Greater Toronto Area: A Formative Study
Bibliographic record
Abstract
Abstract Background As of date, the world is experiencing its highest levels of human displacement, with many families resettling in Canada. While there is an understanding that families of refugee background have complex, multigenerational mental health, and developmental needs, little is known about whether their parenting needs are met adequately in Canada after resettlement nor how COVID-19 related restrictions have further complicated their resettlement experience. Objectives To gather data about the facilitators and barriers that impact the parenting experience of families of refugee background in the Greater Toronto Area (GTA) in the context of their children’s unique developmental and resettlement needs. Design/Methods This was a qualitative, community-based participatory research study. All data was collected from August 2019 to July 2020. In-depth interviews (IDIs) were conducted with caregivers of refugee background and healthcare providers that work closely with this population. Data were recorded, transcribed, and coded by two independent coders and a peer-debriefing strategy was used to verify the coding approach. Results In total, 20 IDIs were conducted (7 caregivers, 13 care providers). Six key themes were identified: (1) Mental Health and Healthcare Access, (2) Language, (3) Navigating Canadian Systems, (4) Finances, (5) Cultural Dimensions of Caregiving, and (6) Advocacy and Agency. The caregivers and healthcare providers agreed that language is a migration barrier, housing and financial stability are challenging to obtain, and that families struggle to self-advocate. There was disagreement as to how families and healthcare providers viewed healthcare access: caregivers reported that they have good access, but the providers cited that there are still barriers, including health literacy and cultural differences. While caregivers believe they are well supported by the government, healthcare providers believe more funding and education on the families’ rights are necessary. Finally, although most caregivers describe Canada as welcoming, healthcare providers highlighted more incidences of misunderstandings and gaps within the system that may allow families to fall through the cracks. Conclusion Our study describes the unique facilitators and barriers to the resettlement, healthcare access, and lived experiences of families of refugee background raising children within the GTA.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".