Building Responsive Intersectoral Initiatives for Newcomers in Toronto: Learning from Service Providers’ Experiences in the Context of COVID‑19
Bibliographic record
Abstract
Background: Newcomer populations in urban centers experienced an exacerbated effect of coronavirus disease 2019 (COVID‑19) due to their precarious living and working conditions. Addressing their needs requires holistic care provisioning, including psychosocial support, assistance to address food security, and educational and employment assistance. Intersectoral collaboration between the public and the community sector can reduce vulnerabilities experienced by these groups. Objective(s): This research 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 Toronto, Ontario, Canada to generate lessons for a sustainable response. Methods: The research uses a participatory governance approach to study multiple qualitative cases (with a case being an intersectoral initiative). We conducted interviews (n = 25) with community and public sector frontline workers and managers, as well as municipal/regional/provincial policymakers and funders. The data were analyzed thematically with an inductive approach. Findings: The analysis covers four key themes: (1) vulnerable newcomers’ circumstances regarding accessing the social determinants of health during COVID‑19; (2) the process of designing specific interventions to target these populations’ needs and service access challenges in the context of COVID‑19; (3) the implementation phase of the initiatives, including any associated challenges and lessons learned; and finally, (4) long‑term potential sustainability of the initiatives. Conclusions: The findings demonstrate that intersectoral initiatives can be implemented to develop a responsive service for marginalized populations; however, their translation beyond pandemic settings would require institutional mechanisms to bring policy shifts to provide a bottom‑up collaborative approach.
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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 | 0.001 | 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 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".