Adaptation under strain: an ethnographic process evaluation of community-based psychosocial support services for refugee claimants during the height of the COVID-19 pandemic in Montreal, Canada
Bibliographic record
Abstract
During the first 2 years of the COVID-19 pandemic in Canada, tens of thousands of refugee claimants faced worsened resettlement stress with limited access to services. Community-based programs that address social determinants of health faced significant disruptions and barriers to providing care as a result of public health restrictions. Little is known about how and if these programs managed to function under these circumstances. This qualitative study aims to understand how community-based organizations based in Montreal, Canada, responded to public health directives and the challenges and opportunities that arose as they attempted to deliver services to asylum seekers during the COVID-19 pandemic. We used an ethnographic ecosocial framework generating data through in-depth semi-structured interviews with nine service providers from seven different community organizations and 13 refugee claimants who were purposively sampled, as well as participant observation during program activities. Results show that organizations struggled to serve families due to public health regulations that limited in-person services and elicited anxiety about putting families at risk. First, we found a central trend in service delivery that was the transition from in-person services to online, which presented specific challenges including (a) technological and material barriers, (b) threats to claimants' sense of privacy and security, (c) meeting linguistic diversity needs, and (d) disengagement from online activities. At the same time, opportunities of online service delivery were identified. Second, we learned that organizations adapted to public health regulations by pivoting and expanding their services as well as fostering and navigating new partnerships and collaborations. These innovations not only demonstrated the resilience of community-organizations, but also revealed tensions and areas of vulnerability. This study contributes to a better understanding of the limits of online service delivery for this population and also captures the agility and limits of community-based programs in the COVID-19 context. Its results can inform decision-makers, community groups and care providers to develop improved policies and program models that preserve what are clearly essential services for refugee claimants.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".