Shared Decision-Making Toolkit: An Effective Strategy for the Continuous Professional Development of Primary Care Nurses
Bibliographic record
Abstract
Context: Shared decision-making (SDM) is central to person-centred care and primary care nurses’ practice. Our previous work showed the unmet educational needs of primary care nurses to engage in SDM with complex care needs patients. Objective: Using Kirkpatrick’s four levels of learning, we sought to provide primary care nurses a continuing professional development (CPD) toolkit that meets their educational needs so they will engage in SDM with complex care needs patients. Study Design: One-group pre-post study to evaluate the CPD toolkit Setting: Primary care clinics in Quebec Population Studied: Primary care nurses Intervention: Cocreated CPD toolkit with a dissemination strategy using social media and the primary care nurses’ virtual community of practice. Outcome Measures: We used validated questionnaires using a 4-points Likert scale to measure Kirkpatrick’s level 1 (reaction) and level 2 (learning) of primary care nurses after using the CPD toolkit. We collected qualitative data to document primary care nurses’ perceptions of the CPD toolkit. Analysis: We used descriptive analyses to characterize the sample and student t-test for paired samples to evaluate the impact of the CPD toolkit. We used thematic analysis for qualitative data. Results: Our CPD toolkit was launched in the fall 2021. 165 primary care nurses completed it. From them, 69 completed pre-and post-training CPD questionnaires. Giving to Level 1, over 90% of the primary care nurses was satisfied with the CPD toolkit. For Level 2, the CPP toolkit significantly improved their confidence (p≤0.001) and intention (p≤0.01) to apply their knowledge in clinical settings. Comparative analyses show that the CDP toolkit appears to be most effective for primary care nurses practicing for 1 to 10 years. Qualitatively, as the most appreciated elements, primary care nurses identified the clarity and conciseness of the content disseminated and the access to concrete tools. After the CDP toolkit completion, primary care nurses perceived themselves as better able to assess patients’ needs, support them in their SDM, and understand the nurse’s and patient’s roles in SDM. Conclusions: This project demonstrated that an innovative model of asynchronous CPD toolkit for SDM and primary care nurses is efficient to improve Kirkpatrick’s Levels of learning 1 and 2 in primary care settings that match the educational needs expressed. This CDP toolkit could be exportable to other clinicians 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 | 0.046 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".