Primary Health Care (PHC) Registered Nurses (RNs) as facilitators of healthcare access for recent immigrants in Ontario: An interpretive descriptive study
Bibliographic record
Abstract
Background and Objectives: Recent immigrants experience unique challenges when accessing health services. Registered Nurses (RNs) working in primary health care (PHC) have the competencies and are well-positioned to facilitate healthcare access for immigrants. Aim: This study explored how PHC-RNs in Ontario support healthcare access and address barriers among recent immigrants. Methods: This study used a qualitative, interpretive descriptive approach and was informed by Levesque et al.’s (2013) access to care framework. Semi-structured interviews were conducted with 10 PHC RNs practising in Toronto, Ottawa-Gatineau, Hamilton, Kitchener-Cambridge-Waterloo, and London. Data were collected and analyzed concurrently using an inductive and deductive approach. Results: Findings show that PHC-RNs play instrumental roles in supporting recent immigrants through facilitating healthcare access across 9 dimensions of Levesque et al.’s (2013) access framework: (1) appropriateness, (2) availability and accommodation, (3) ability to perceive, (4) ability to engage, (5) ability to seek, (6) ability to reach, (7) affordability, (8) ability to pay, and (9) acceptability. RNs addressed significant challenges to health service access for recent immigrants, including language, geographical, and financial barriers. Several opportunities were identified to enhance the PHC RN clinical practice role to improve the delivery of PHC for recent immigrants, such as increasing organizational resources, utilizing their expertise and role beyond the PHC clinic, and promoting culturally appropriate care. Conclusions: PHC-RNs are key facilitators of healthcare access for recent immigrants by coordinating their care, educating, and connecting this population to services across the health system. However, there are opportunities to optimize nursing roles and more effectively utilize their scope of practice within interdisciplinary teams to promote the health of immigrants.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".