Characteristics of refugee healthcare models: A scoping review protocol
Bibliographic record
Abstract
Background: Canada is facing the arrival of unparalleled numbers of refugees. Access to quality healthcare is central to ensuring optimal integration into Canadian society (Iqbal et al., 2022). However, their ability to receive care coverage is fragmented by systemic barriers and profound health challenges. Refugee community health centers (CHCs) have been established to provide wrap-around services to refugees upon their arrival (Albrecht, 1998). However, there is sparse scholarly research on their effectiveness in Canada and the existing models that they operate under. Method: A scoping review will be conducted using the PRISMA-ScR checklist for essential reporting (Tricco et al., 2018). In collaboration with a research librarian, articles from four databases will be gathered and uploaded onto Covidence. Grey literature will be incorporated manually. A comprehensive set of inclusion/exclusion criteria will be formulated to screen study titles and abstracts, followed by a thorough review of full texts and data extraction. Significance: Given the increase in refugee resettlement, their unique health needs, and barriers to accessing care, this research will contribute to a limited body of literature by highlighting facilitators to primary healthcare access. This research will be mobilized with refugee community health centres, decision-makers, and the government to inform culturally safe, equitable healthcare practices in Alberta and across Canada. Finally, these findings will inform CHCs of alternate provision of care models. Key words: refugee, community health centre, healthcare, scoping review, access, service models
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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 | 0.139 | 0.143 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
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".