7.2-O8What are the values and preferences toward primary healthcare of newly arriving refugees and other migrants? A Discrete Choice Experiment
Bibliographic record
Abstract
Background: Newly arrived migrants arrive to new healthcare with hopes and expectations but also subconscious cultural beliefs and past experiences. This study examines how diverse migrants’ values influence their primary healthcare seeking choices. Methods: We identified a sample of newly arriving migrants from 11 countries in collaboration with settlement organizations in Ottawa and Gatineau, Canada. Following a pilot phase, we posed a series of choice questions (cost, time, interpreter services, mode of delivery) within a discrete choice experiment related to a semi-urgent condition. We analyzed data using logistic regression analysis, evaluating the factors and interactions between them. Results: Over 3000 choice scenarios representing 102 newly arrived refugees were collected. Preliminary results suggest that there is a strong preference for in-person services rather than over the phone communication when controlling for other factors, including oral interpretation. Secondary preferences included lower costs and shorter waiting times. Conclusions: The strong in-person preference stands as a barrier to use of tele-health services by this population. The need for trust and personal relationships may be factor, but more research is needed to better design and deliver primary healthcare services and policies. Main message: Newly arriving migrants prefer in-person consultations even more than interpretation. Secondary preferences included lower costs and shorter waiting times.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".