Long-Term Effects of Individual-Focused and Team-Based Training on Health Professionals’ Intention to Have Serious Illness Conversations: A Cluster Randomised Trial
Bibliographic record
Abstract
We aimed to measure the sustainability of health professionals’ intention to have serious illness conversations with patients using the Serious Illness Conversation Guide (SICG) after individual-focused training versus team-based training. In a cluster randomised trial, we trained healthcare professionals in 40 primary care clinics and measured their intention to hold serious illness conversations immediately (T1), after 1 year (T2) and after 2 years (T3). Primary care clinics (n = 40) were randomly assigned to individual-focused training (comparator) or team-based training (intervention). Average age of the 373 participants was 35–44 years, 79% were women. On a scale of 1 to 7, at T1, the mean intention was 5.33 (SD 0.20) in the individual-focused group and 5.36 (SD 0.18) in the team-based group; at T2, these scores were 4.94 (SD 0.23) and 4.87 (SD 0.21) and at T3, 5.14 (SD 0.24) and 4.59 (SD 0.21), respectively. At T3, the difference in mean intention between study groups had a significant p-value of 0.01. Intention to have serious illness conversations was lower at T2 and T3 after team-based training than after individual-focused training, with a significant difference at 2 years in favour of individual-focused training. Health professionals reported not enough time during consultations for serious illness conversations as a major barrier.Registration number: ClinicalTrials.gov (ID NCT03577002) for the parent clinical trial.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".