Implementation of an intersectoral outreach and community nursing care intervention with refugees in Quebec: A protocol study
Bibliographic record
Abstract
AIM: To establish and assess an intersectoral local network focused on the roles of registered nurses and primary healthcare nurse practitioners to ensure the continuity of care and service pathways for refugees in Quebec. DESIGN: Developmental evaluation with a mixed methodology. METHODS: The qualitative component will include: (1) a document review; (2) observations of participants during meetings of different governance structures; (3) semi-structured interviews with key actors (n = 40; 20/neighbourhood interventions); and (4) focus groups with end users of the services (refugees) (n = 4; 6 to 8 participants per group). The quantitative component will be based on: (1) a data sheet on health and social interventions for refugees users filled in by registered nurses, primary healthcare nurse practitioners and physicians and (2) data analysis of the clinical-administrative database since 2012. This study received funding in June 2019 and Research Ethics Committee approval was granted in July 2020. DISCUSSION: In Quebec, refugee vulnerability is exacerbated by the lack of integration of existing resources and the lack of access to care and continuity of services. To address these issues, an integrated local network for refugees must be developed. Additionally, we will explore the role of registered nurses and their collaboration with primary healthcare nurse practitioners. IMPACT: This study will provide recommendations on how to optimize the scopes of practice of registered nurses and primary healthcare nurse practitioners, adapt care and services and develop a local intersectoral network to better meet the complex needs of refugees. It will evaluate the use and the appreciation of new services for targeted populations (neighbourhoods and refugees) and aim to improve the accessibility, continuity and user experience of all health services for those populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".