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
Bibliographic record
Abstract
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 ).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.134 | 0.161 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".