Healthcare Professionals’ Intention to Engage in Serious Illness Conversations After Training: A Secondary Analyses of a cRCT
Bibliographic record
Abstract
Context: Few studies have evaluated the impact of an interprofessional advance care planning (ACP) intervention in primary care. A structured ACP training as part of the implementation of the Serious Illness Care Program (SICP) was adapted to include an interprofessional team-based approach to ACP. Objective: To evaluate the impact of being trained in an Interprofessional team-based approach to ACP compared to being trained in an individual clinician-based ACP approach on primary healthcare professionals’ (HCP’s) intention to engage patients in serious illness conversations Study design and analysis: We conducted a comparative effectiveness study using post-interventions measures from a cluster-randomized clinical trial Setting: community-based primary care practices (PCPs) in the United States and in Canada recruited from 7 practice-based research networks (PBRNs) that are part of the Meta-Larc consortium. The unit of randomization was the PCPs stratified by PBRN. Population: HCP’s recruited through primary care practices. Intervention: Practices were assigned to either an interprofessional team-based training (intervention) or individual clinician-based (comparator). Both trainings were adapted from the SICP developed by Ariadne Labs and lasted 3 hours (1.5h online tutorial and 1.5h in-person role-play session). Outcome Measures: The intention of primary HCP’s to have serious illness conversations after being trained in an interprofessional team-based approach or an individual clinician-based approach of the SICP, measured using the CPD-REACTION questionnaire. Results: 38 of 45 (84.4%) practices participated and 373 of 535 (69.7%) HCP’s fully answered the CPD-REACTION in the study (64.1% under 44 years old; 78.0% women; 85.0% at least 4-years university studies 71.6.2% were primary care clinicians; 53.9% in urban settings). After training, mean intention scores for the interprofessional team-based (n=223) and individual clinician-based (n=150) were 6.0 ± 1.1 and 6.5 ± 0.7, respectively. Mean difference was -0.45 (CI -0.79; -0.11; p=0.01). Adjusted for education level and profession, mean difference was -0.05(CI -0.38;0.29); p=0.77). Conclusions: Participants in the interprofessional team-based training did not perform better than the individual clinician-based approach in impacting healthcare professionals’ intentions to have serious illness conversations. Profession and education may have a role in the results found.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".