ICT-Refugee: The development, implementation, and evaluation of an integrated care team to support refugee patients as they transition from temporary to permanent primary care
Bibliographic record
Abstract
Upon arrival to Canada, government assisted refugees typically can access settlement services and universally funded health care; health care may be delivered through refugee health clinics, which are meant offer temporary care until patients are stable and able to transition to a permanent primary care practice (PCP). In Southern Ontario, Canada, we evaluated the development and implementation of an integrated care team (ICT-Refugee) that supports refugee patients and receiving clinics in this transition. Several refugee health and service organizations partnered with the local Ontario Health Team (the regional health administrative body) to offer this program. All Ontario Health Teams have patient partners who attend strategic planning sessions and approve programming. This initiative was also guided by ICT members (see below), some of whom are refugees themselves, and who shared their perspectives on what would and would not work in their respective communities. Launched in January 2022, the ICT-Refugee program includes access to an on-demand interpretation service, and the interdisciplinary ICT. Members of the team include: two discharge and intake coordinators (at refugee health clinics), a case manager, a pharmacist, three “newcomer system navigators”, and representatives from home and community care services and a refugee settlement agency. To date (the program and evaluation are ongoing), the ICT has transitioned 499 patients to 15 primary care practices. All 499 patients were offered access to the ICT, and 41 self-selected or were referred by their new practice to receive more intensive, interdisciplinary support from the ICT (8%). Our evaluation team has observed 22 ICT meetings, composed field notes, and consolidated program statistics. To understand the development and impact of the program, we interviewed all ICT staff (n=9), and six patients (in three languages, with interpreters) . Interviews were digitally recorded, then anonymized and uploaded into NVivo 12 for thematic analysis. The 41 patients requiring ICT supports in 2022 (Jan-Nov.) received 396 hours of interdisciplinary care/supports over 833 sessions. Patients had high and diverse needs. Approximately 20% of these hours were spent directly linking or referring patients to community resources. In addition to navigating the medical transition, the ICT supported patients with education, employment, finances, mental health, transportation, social isolation, and other needs. Staff noted that it was easier to attend to the patients’ medical needs (e.g. getting to their appointments), once the basics of survival (e.g. food, housing) were addressed. The evaluation identified many lessons learned in the first year, including: expect the development and refinement of an ICT program to take time (it cannot be designed, refined, and implemented with demonstrated impact in one year); embedding interpretation services into all aspects of the program was essential; it can be challenging to find clinics willing to accept refugee patients; patients are unique and will required a tailored approach and care plan; and, these types of programs are essential for bridging health and social care services, which in our region had previously been operating in silos. ICT support of refugee patients is ongoing. PCPs will be interviewed in the next phase of the evaluation.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".