Profiles and pathways of care in situations of vulnerability: what we learn from the journey of refugees and asylum seekers within the refugee clinics (RC) in Quebec
Bibliographic record
Abstract
Although primary and secondary access to healthcare and services for migrants is considered an essential component of their integration into host societies, their pathways through the health system remain marked by obstacles. However, the migrant population is not a homogeneous group, and the barriers observed in access to care may be exacerbated within certain vulnerable subgroups. This is particularly the case for refugees and asylum seekers. As part of this proposal, we benefit from the existence of valuable administrative data (iCLSC) that has informed the progress of refugees and asylum seekers within RC in Quebec since January 1, 2012, to describe and analyze their profiles and service pathways. We mainly try to assess to what extent the trajectories observed in the consultations of refugees and asylum seekers (both in terms of healthcare professionals consulted and reasons for consultation) reflect or not the relevance, coherence and adaptation of services and pathways to meet their needs. We mobilize a quantitative approach through three methods of analysis: (i) a descriptive analysis of the profiles of care making it possible to associate the socio-demographic characteristics of refugees and asylum seekers with two indicators of the course of care (healthcare professionals consulted and reasons for consultations); (ii) an analysis of the sequences making it possible to describe and identify the standard profiles of care pathways; (iii) a multinomial regression analysis to identify the factors associated with the different types of pathways. Finally, our proposal wishes to answer the question structuring theme 5 of the Conference: How can we reach the most vulnerable groups to ensure an inclusive approach?
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".