Impacts of an online asynchronous continuing professional development toolkit supporting primary care nurses to engage in shared decision-making: a single-group pre-post study
Bibliographic record
Abstract
BACKGROUND: Shared decision-making (SDM) is central to person-centred care and professional nursing practice. Some primary care nurses must become more comfortable and prepared to use SDM in their practice, especially with patients having complex care needs. METHODS: We conducted a single-group pre-post study with primary care nurses to assess the relevance and impacts of the online continuing professional development (CPD) toolkit. Using the New World Kirkpatrick model, we assessed the toolkit's relevance (level-1, reaction) and nurses' confidence and commitment (level-2, learning). We collaborated with the virtual community of practice for nurses in family medicine groups in Quebec to reach out to as much nurses as possible. We sent hard copies of the toolkit to 42 primary care establishments. We used descriptive statistics and the student t-test to treat quantitative data and analyzed open-ended questions with qualitative content analysis. RESULTS: One hundred sixty-five nurses used the toolkit, and 69 completed the pre- and post-training survey. Most were female (94.2 %), aged between 31-45 years old (55.1 %), and held a first university degree (91.3 %). Ninety-six percent (96 %) agreed or strongly agreed that the toolkit would improve their practice. The toolkit significantly increased nurses' confidence (p ≤ 0.001) and intention (p ≤ 0.01) to engage in SDM with patients having complex care needs. Nurses appreciated the relevance of video vignettes and accessibility, amongst others. CONCLUSIONS: Primary care nurses felt better able to include SDM in their practice with patients with complex care needs and understand their roles better. A CPD toolkit by and for primary care nurses is relevant and increases learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".