Canadian Post-licensure Education for Primary Care Nurses Addressing the Patient’s Medical Home Model and Canadian Competencies for Registered Nurses in Primary Care: An Environmental Scan
Bibliographic record
Abstract
Purpose: Nurses constitute the largest non-medical primary care workforce in Canada and play an integral role in promoting health equity and improving access, continuity of care, patient satisfaction, and clinical outcomes. In 2019, the Canadian Family Practice Nurses Association (CFPNA) published Canadian competencies unique to registered nurses (RNs) in primary care. However, primary care–focused content has not been well integrated into Canadian nursing curriculum, and additional education is required to enable nurses to enact these competencies in practice. To address this gap, the purpose of this paper was to identify post-licensure education programs available to nurses in primary care across Canada and explore their alignment with the CFPNA Competencies for RNs in Primary Care and the College of Family Physicians of Canada Patient’s Medical Home (PMH) model. Method: An environmental scan was conducted, consisting of a literature review and expert consultations. The literature review involved a search of electronic databases (CINAHL Plus, MEDLINE via EBSCOhost) using relevant keywords/search strings and grey literature collected from websites of academic institutions, government/professional organizations, and nursing regulatory bodies. Studies considered for inclusion reported on programs available to RNs and nurse practitioners involving high-level primary care education, with properties related to the CFPNA competencies and/or PMH model. Data were extracted and grouped according to the type of program, content, targeted skills/knowledge, CFPNA competency domains and/or PMH model pillars, and delivery methods. Expert consultations involved data verification by key informants and an electronic questionnaire. Key informants, who had expertise in primary care, nursing, and/or continuing education, were contacted via email and asked to verify data retrieved from the literature review. An electronic questionnaire (via Qualtrics) was sent to primary care nurses/administrators to gather additional data on education programs and identify factors that support or hinder nurse involvement. Responses were categorized narratively based on overarching themes. Results: Ten unique programs were identified across 12 sources. All identified programs offered high-level primary care content that was generally tailored to specific practice areas (e.g., chronic disease management). Programs addressed some of the CFPNA competency domains (i.e., clinical practice; leadership) and the PMH model pillars (i.e., patient- and family-centred care; training, education, and continuing professional development; measurement, continuous improvement, and research). Courses contained theory and/or clinical components and most were limited to provincial-level delivery. A total of 63 respondents completed the electronic questionnaire across multiple provinces and reported that education programs differed within provinces and that there was no required education to practise in primary care beyond entry-to-practice preparation. Key factors that supported or hindered their ability to participate in education were identified, and consensus was expressed that a national, standardized program tailored to the nursing role in primary care is needed. Conclusion: These findings highlight notable gaps in primary care nursing education and emphasize the need for an education program that aligns with established primary care frameworks to guide clinical practice, nurses’ scope of practice, and their unique contributions to primary care. A standardized education program has the potential to improve quality of patient care, increase nurse satisfaction, and enhance overall collaborative practice within team-based care.
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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 | 0.025 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.050 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".