Development and Evaluation of Serious Illness Conversation Training for Interprofessional Primary Care Teams
Bibliographic record
Abstract
Background:Early advance care planning (ACP) conversations are essential to deliver patient-centered care. While primary care is an ideal setting to initiate ACP, such as Serious Illness Conversations (SICs), many barriers exist to implement such conversations in routine practice. An interprofessional team approach holds promises to address barriers. Design:An existing SIC training was adapted for IP-SIC and then implemented and evaluated for acceptability and effectiveness. Measures:Acceptability of the IP-SIC training and participants' self-reported likelihood to engage in ACP after the training. Results:The 156 participants were a mix of physicians and advanced practice providers (APPs) (44%), nurses and social workers (31%), and others (25%). More than 90% of all participants rated the IP-SIC training positively. While nurse/social worker and other groups were less likely than physician and APP group to engage in ACP before training (4.4, 3.7, and 6.4 on a 1–10 scale, respectively), all groups showed significant increase in likelihood to engage in ACP after the IP-SIC training (8.5, 7.7, and 9.2, respectively). Both physician/APP and nurse/social worker groups showed significant increase in likelihood to use the SIC Guide after the IP-SIC training, whereas an increase in likelihood to use SIC Guide among other groups was not statistically significant. Conclusion:The new IP-SIC training was well accepted by interprofessional team members and effective to improve their likelihood to engage in ACP. Further research exploring how to facilitate collaboration among interprofessional team members to maximize opportunities for more and better ACP is warranted.ClinicalTrials.gov ID: NCT03577002
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".