Sustainability of health professionals' intention to have serious illness conversations at 1 and 2 years after training
Bibliographic record
Abstract
We know little about the sustainability of CPD impact over time. Objective: To measure the sustainability of health professionals9 intention to have conversations about serious illness after CPD with an individual-focused approach compared to one with an interprofessional team-based approach. Method: We conducted a cluster randomized trial with measures immediately (T1), at 1 year (T2) and at 2 years (T3) after training in primary care clinics in Canada and the United States. Results are reported according to CONSERVE (2021) guidelines. Clinics were randomly assigned to either individual-focused training (comparator) or team-based training (intervention). We measured health professionals9 intention to have serious illness conversations, associated psychosocial factors (social norm, moral norm, beliefs about consequences, and beliefs about abilities) using the CPD-Reaction. We also assessed participants perception of the SICG as well as sociodemographic characteristics. Statistical analyses were performed using a linear mixed model for each time point (T1, T2 and T3) with an interaction term between time point and arm. Results: The average age of the 373 participants was between 35 and 44 years, and 79% were women at each time point. On a scale of 1 to 7, at T1 the mean intention was 5.33 (SD 0.20)) for the individual-focused arm and 5.36 (SD 0.18) for the team-based arm; at T2, 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. The difference in mean intention between the two study arms was 0.02 (CI -0.26 to 0.31), -0.07 (CI -0.49 to 0.34), -0.55 (-1.00 to -0.10) at T1, T2 and T3 respectively with a p-value of 0.01 at T3. The p-value for the interaction between study arm and time point was 0.048. Overall, participants felt confident in their ability to have serious illness conversations with the SICG but time constraints and appropriateness of the clinical encounter were identified as barriers. Conclusion: Health professionals9 intention to have serious illness conversations was lower at 1- and 2-year follow-up after training using an interprofessional approach compared to an individual-based approach. There is a significant difference at two years in favor of individual-focused training. Our results could contribute to improving CPD and, in turn the quality-of-care provision.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".