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Record W4391729535 · doi:10.1101/2024.02.05.24302368

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 of a cluster randomized trial

2024· preprint· en· W4391729535 on OpenAlexaff
Lucas Gomes Souza, Patrick Archambault, Dalil Asmaou Bouba, Suélène Georgina Dofara, Sabrina Guay-Bélanger, Sergio Cortez Ghio, Souleymane Gadio, LeAnn Michaels, Jean‐Sébastien Paquette, Shigeko Izumi, Annette M Totten, France Légaré

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCentre hospitalier universitaire de QuébecCentre intégré de santé et de services sociaux de Chaudière-AppalachesCentre intégré universitaire de santé et de services sociaux de la Capitale-NationaleUniversité LavalCentres Intégré Universitaires de Santé et de Services Sociaux
Fundersnot available
KeywordsRandomized controlled trialCluster randomised controlled trialHealth carePsychologyIntervention (counseling)MedicineFamily medicineNursing

Abstract

fetched live from OpenAlex

ABSTRACT Background Cluster Randomized Trials (cRTs) conducted in real-world settings face complex challenges due to diverse practices and populations. Process evaluations alongside cRTs can help explain their results by exploring possible causal mechanisms as the trial proceeds. Objective To conduct a process evaluation alongside a cRT that compared 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 training program (comparator) and measured primary outcomes at the patient level: days at home and goal of care. Our theory-informed mixed-methods process evaluation alongside the cRT measured intention to have serious illness conversations with patients among the trained PHCPs using the CPD-Reaction tool. Barriers and facilitators to implementing serious illness conversations were identified through open-ended questions and analyzed using the Theoretical Domains Framework. We used the COM-B framework to perform triangulation of data. We reported results 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: 0.11) and 6.42 (Standard Error: 0.13), respectively. Mean difference between arms was 0.0 (95% CI −0.30;0.29; 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. Future team-based interventions could foster behaviour adoption 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)0.134
metaresearch head score (Gemma)0.161
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.134
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.161
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0060.009
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0100.001

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 head0.414
GPT teacher head0.602
Teacher spread0.188 · 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 designRandomized 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

Citations2
Published2024
Admission routes1
Has abstractyes

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