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Record W4407338536 · doi:10.1186/s12912-024-02569-6

Development and preliminary evaluation of a decision coach training module for nurses in Norway

2025· article· en· W4407338536 on OpenAlexaff
Simone Kienlin, Kari Nytrøen, Jürgen Kasper, Dawn Stacey

Bibliographic record

VenueBMC Nursing · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersHelse Nord RHFUniversitetet i Tromsø
KeywordsCoachingDecision aidsMedicineDescriptive statisticsMedical educationCurriculumSample (material)NursingHealth careQualitative propertyDecision support systemApplied psychologyPsychologyComputer scienceAlternative medicineArtificial intelligencePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Shared decision-making (SDM) is a collaborative patient-centred process for arriving at informed healthcare decisions. Decision coaching can help support SDM when combined with patient decision aids. As part of a meta-curriculum "Ready for SDM" for training different healthcare professionals in SDM, we developed and pilot-tested a new module designed to train nurses as decision coaches. The study assessed nurses' perceptions of a decision coach training module, focusing on its feasibility, acceptability and its role in developing decision coaching capabilities. METHODS: We used a two-phase approach guided by the Knowledge-to-Action Framework. In the first phase, we developed a decision coach training module. The second phase involved preliminary testing, using a descriptive design with qualitative and quantitative methods. We recruited a convenience sample of participants from two hospitals. Participants completed questionnaires at the end of Part A (classroom training). The assessment was informed by Kirkpatrick's first three levels of educational outcomes: reaction (acceptability), learning (self-reported attitudes, intentions and confidence) and behaviour (practical application of decision coaching). A post-hoc inquiry investigated low participation in Part B of the coach training. Qualitative data underwent content analysis and quantitative data were analysed using descriptive statistics. RESULTS: The development resulted in a decision coach training comprising a Part A (6 h) on SDM and decision coaching fundamentals and a Part B (1 h) which involved practical application of decision coaching in the participants' own practice (audio recorded) with self-appraisal and individualised feedback. In preliminary testing with 19 nurses from seven clinical departments, 90% of participants rated Part A as acceptable and relevant to practice. Only one nurse completed Part B due to reluctance to audio record coaching sessions. The most reported perceived barrier was time constraints. Key perceived facilitators identified were interprofessional collaboration, management support and additional practical training. CONCLUSION: Decision coach training was feasible to deliver in the classroom. Participants reported Part A as acceptable and relevant to their practice. The second part, including an audio recording component, proved unfeasible. Further research should explore alternative methods for skill assessment and feedback in clinical practice. The results from this study will inform further refinement of the Norwegian Ready for SDM meta-curriculum and implementation strategies, particularly regarding the practical training components. TRIAL REGISTRATION: Retrospectively registered (14.02.2023) at ISRCTN (ISRCTN44143097).

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)0.009
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.412
GPT teacher head0.520
Teacher spread0.108 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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