Learning from intersectoral initiatives to respond to the needs of refugees, asylum seekers, and migrants without status in the context of COVID-19 in Quebec and Ontario: a qualitative multiple case study protocol
Bibliographic record
Abstract
BACKGROUND: Refugees, asylum seekers, and migrants without status experience precarious living and working conditions that disproportionately expose them to coronavirus disease 2019 (COVID-19). In the two most populous Canadian provinces (Quebec and Ontario), to reduce the vulnerability factors experienced by the most marginalized migrants, the public and community sectors engage in joint coordination efforts called intersectoral collaboration. This collaboration ensures holistic care provisioning, inclusive of psychosocial support, assistance to address food security, and educational and employment assistance. This research project explores how community and public sectors collaborated on intersectoral initiatives during the COVID-19 pandemic to support refugees, asylum seekers, and migrants without status in the cities of Montreal, Sherbrooke, and Toronto, and generates lessons for a sustainable response to the heterogeneous needs of these migrants. METHODS: This theory-informed participatory research is co-created with socioculturally diverse research partners (refugees, asylum seekers and migrants without status, employees of community organizations, and employees of public organizations). We will utilize Mirzoev and Kane's framework on health systems' responsiveness to guide the four phases of a qualitative multiple case study (a case being an intersectoral initiative). These phases will include (1) building an inventory of intersectoral initiatives developed during the pandemic, (2) organizing a deliberative workshop with representatives of the study population, community, and public sector respondents to select and validate the intersectoral initiatives, (3) interviews (n = 80) with community and public sector frontline workers and managers, municipal/regional/provincial policymakers, and employees of philanthropic foundations, and (4) focus groups (n = 80) with refugees, asylum seekers, and migrants without status. Qualitative data will be analyzed using thematic analysis. The findings will be used to develop discussion forums to spur cross-learning among service providers. DISCUSSION: This research will highlight the experiences of community and public organizations in their ability to offer responsive services for refugees, asylum seekers, and migrants without status in the context of a pandemic. We will draw lessons learnt from the promising practices developed in the context of COVID-19, to improve services beyond times of crisis. Lastly, we will reflect upon our participatory approach-particularly in relation to the engagement of refugees and asylum seekers in the governance of our research.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: yes · About a Canadian topic: yes | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: yes · About a Canadian topic: no | Qualitative | high |
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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".