An Online Training Program to Improve Clinicians’ Skills in Communicating About Serious Illness
Bibliographic record
Abstract
BACKGROUND: Large-scale efforts to train clinicians in serious-illness communication skills are needed, but 2 important gaps in knowledge remain. (1) No proven training method exists that can be readily scaled to train thousands of clinicians. (2) Though the value of interprofessional collaboration to support incapacitated patients' surrogates is increasingly recognized, few interventions for training intensive care unit (ICU) nurses in important communication skills can be leveraged to provide interprofessional family support. OBJECTIVE: To develop and test a web/videoconference-based platform to train nurses to communicate about serious illness. METHODS: A user-centered process was used to develop the intervention, including (1) iteratively engaging a stakeholder panel, (2) developing prototype and beta versions of the platform, and (3) 3 rounds of user testing with 13 ICU nurses. Participants' ratings of usability, acceptability, and perceived effectiveness were assessed quantitatively and qualitatively. RESULTS: Stakeholders stressed that the intervention should leverage interactive learning and a streamlined digital interface. A training platform was developed consisting of 6 interactive online training lessons and 3 group-based video-conference practice sessions. Participants rated the program as usable (mean summary score 84 [96th percentile]), acceptable (mean, 4.5/5; SD, 0.7), and effective (mean, 4.8/5; SD, 0.6). Ten of 13 nurses would recommend the intervention over 2-day in-person training. CONCLUSIONS: Nurses testing this web-based training program judged it usable, acceptable, and effective. These data support proceeding with an appropriately powered efficacy 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".