The Impact of the Patient trainer on the Integration of the Patient Engagement Approach in Family Medicine Group
Bibliographic record
Abstract
Context: In Canada, primary health care is usually the first point of contact for people living with health disease. Among these primary care settings are primary care clinics (PCC), where the role of registered nurses (RN) is needed. However, nursing practice in PCC is variable and sometimes suboptimal from clinic to clinic. In 2019, the Quebec Ministry of Health and Social Services deployed a practical guide for RN practicing in PCC. This guide was intended to support best professional and interprofessional practices in PCC and enhance the quality of services offered in PCC according to a physical-social vision of care and interprofessional collaboration but also in partnership with the patient. The Formation de formateurs en première ligne (F2PL) project team developed a train-the-trainer educational intervention to support the content appropriation of this guide. This educational intervention is uncommon because it includes patients as trainers (PT) in the trainer group. PT provided andragogic contents about patient experiences to enhance patient engagement. Objective: To describe the changes in nurse’s patient engagement practices working with patients as a result of the training provided by the patient trainers. Study Design and Analysis: A descriptive qualitative approach was used to provide an in-depth description of changes in RN’s practices as a result of F2PL educational intervention. Individual interviews were conducted with RN and PT to explore the changes in practice achieved by the registered nurses, as well as the barriers and facilitators to the adoption of this new practice and to explore PT’s role across educational intervention. An inductive and deductive thematic analysis was carried out according to a conceptual model about patient engagement (the Montreal model), and emerging themes were condensed into propositions. A peer review was conducted by the research team including a patient co-leader to ensure credibility. Setting and Population studied: In a semi-urban region, 10 RN and 3 PT were interviewed. Results: The intervention provided by PT have impacted RN in 3 ways: awareness or reminding of known principles, updating commitment to already known principles and enhancing professional development of new skills Conclusions: Patient trainers could help RNs to improve their patient engagement practices in primary 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".