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Record W4317895470 · doi:10.1370/afm.21.s1.3941

Shared Decision-Making Toolkit: An Effective Strategy for the Continuous Professional Development of Primary Care Nurses

2023· article· en· W4317895470 on OpenAlexaboutno aff
Marie-Ève Poitras, Catherine Hudon, Pierre Pluye, Vanessa T. Vaillancourt, Annie Poirier, Andréanne Bernier, France Légaré

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary careComputer scienceProfessional developmentProcess managementKnowledge managementMedical educationMedicineEngineeringFamily medicine

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.046
metaresearch head score (Gemma)0.079
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.046
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.079
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.004
Scholarly communication0.0060.007
Open science0.0040.023
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0100.003

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.

Opus teacher head0.046
GPT teacher head0.471
Teacher spread0.425 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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