Assessing Virtual Mental Health Access for Refugees during the COVID-19 Pandemic Using the Levesque Client-Centered Framework: What Have We Learned and How Will We Plan for the Future?
Bibliographic record
Abstract
During the COVID-19 pandemic, mental health services rapidly transitioned to virtual care. Although such services can improve access for underserved populations, they may also present unique challenges, especially for refugee newcomers. This study examined the multidimensional nature of access to virtual mental health (VMH) care for refugee newcomers during the COVID-19 pandemic, using Levesque et al.'s Client-Centered Framework for Assessing Access to Health Care. One hundred and eight structured and semi structured interviews were conducted in four Canadian provinces (8 community leaders, 37 newcomer clients, 63 mental health or service providers or managers). Deductive qualitative analysis, based on the Client-Centered Framework, identified several overarching themes: challenges due to the cost and complexity of using technology; comfort for VMH outside clinical settings; sustainability post-COVID-19; and communication and the therapeutic alliance. Mental health organizations, community organizations, and service providers can improve access to (virtual) mental health care for refugee newcomers by addressing cultural and structural barriers, tailoring services, and offering choice and flexibility to newcomers.
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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 | 0.007 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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, 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".