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Record W4408864629 · doi:10.1371/journal.pone.0298994

Impact of a team-based versus individual clinician-focused training approach on primary healthcare professionals’ intention to have serious illness conversations with patients: A theory-informed process evaluation embedded within a cluster randomized trial

2025· article· en· W4408864629 on OpenAlexafffund
Lucas Gomes Souza, Patrick Archambault, Dalil Asmaou Bouba, Suélène Georgina Dofara, Sabrina Guay-Bélanger, Sergio Cortez Ghio, Souleymane Gadio, Shigeko Izumi, LeAnn Michaels, Jean‐Sébastien Paquette, Annette M Totten, France Légaré

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCentre hospitalier universitaire de QuébecCentre intégré de santé et de services sociaux de Chaudière-AppalachesCegep regional de LanaudiereUniversité LavalCentres Intégré Universitaires de Santé et de Services Sociaux
FundersFonds de Recherche du Québec - SantéPatient-Centered Outcomes Research Institute
KeywordsRandomized controlled trialIntervention (counseling)Health carePsychologyCluster randomised controlled trialMedicineBest practiceCluster (spacecraft)Family medicineNursingComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Cluster randomized trials (cRTs) on the effectiveness of training programs face complex challenges when conducted in real-world settings. Process evaluations embedded within cRTs can help explain their results by exploring possible causal mechanisms impacting training effectiveness. OBJECTIVE: To conduct a process evaluation embedded within a cRT by comparing the impact of team-based vs. individual clinician-focused SICP training on primary healthcare professionals' (PHCPs) intention to have serious illness conversations with patients. METHODS: The cRT involved 45 primary care practices randomized into a team-based (intervention) or individual clinician-focused (comparator) training program and measured primary outcomes at the patient level: days at home and goals of care. To perform this theory-informed mixed-methods process evaluation embedded within the cRT, a different outcome was measured at the level of the PHCPs, namely, PHCPs' intention to have serious illness conversations with patients as measured with CPD-Reaction. Barriers and facilitators to implementing the conversations were identified through open-ended questions and analyzed using the Theoretical Domains Framework. The COM-B framework was used to triangulate data. Results were reported using the CONSORT and GRAMMS reporting guidelines. RESULTS: Of 535 PHCPs from 45 practices, 373 (69.7%) fully completed CPD-Reaction (30.8% between 25-34 years old; 78.0% women; 54.2% had a doctoral degree; 50.1% were primary care physicians). Mean intention scores for the team-based (n = 223) and individual clinician-focused arms (n = 150) were 5.97 (standard error (SE): 0.11) and 6.42 (SE: 0.13), respectively. Mean difference between arms was 0.0 (95% CI -0.29; 0.30; p = 0.99) after adjusting for age, education and profession. The team-based arm reported barriers with communication, workflow, and more discomfort in having serious illness conversations with patients. CONCLUSIONS: Team-based training did not outperform individual clinician-focused in influencing PHCPs' intention to have serious illness conversations. This process evaluation suggests that team-based training could improve intervention effectiveness by focusing on interprofessional communication, better organized workflows, and better support and training for non-clinician team members. Registration: ClinicalTrials.gov (ID: NCT03577002).

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)0.088
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.468

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.108
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.417
GPT teacher head0.574
Teacher spread0.157 · 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 designNon-randomized trial
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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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