Understanding access to primary health care for newcomers within integrated care models in Ontario, Canada
Bibliographic record
Abstract
Background: With rising immigration rates across Canada, there is increasing demand to tailor integrated primary care to the needs of newcomers. The Eastern York Region North Durham (EYRND) Ontario Health Team (OHT) in Ontario, Canada is therefore implementing a Newcomer Engagement Initiative with two goals: to better understand how newcomers in the region access primary care, and to improve integrated primary care program development and service delivery. In 2022, approximately 8% of the EYRND OHT’s attributed population consisted of newcomers who immigrated in the past 10 years. About 16% of these newcomers did not have a primary care physician, in contrast to 9.2% for the rest of the population. At the ICIC24 Conference, I will share details of this initiative including the identified barriers, facilitators, experiences, and interactions that our newcomers have when accessing services within the region. Methods: Our Newcomer Engagement Initiative was developed in collaboration with the OHT’s patient, family, caregiver and community advisory council, community partners, health care professionals, primary care, and health administration. To date, our team has developed the engagement initiative and begun recruitment for 3 to 5 focus group discussions with about 6 to 9 participants each. Focus group discussions will be held according to the composition of newcomers and the four most common non-English languages spoken in the region: Mandarin, Cantonese, Persian (Farsi), and Tamil. Results: Participants will be asked about different dimensions of access to care, including their health care needs, health care seeking practices, ability to reach health care, and health care utilization. The focus group discussion data will be thematically analyzed according to the identified dimensions of health care access. Discussion: The lessons learned from our Newcomer Engagement Initiative will inform how the OHT and OHT partners can modify existing integrated care programs to better serve newcomers’ primary health care needs, and how to tailor future programs to address these needs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".