Assessment of communication skills in health professions education; Ottawa 2024 consensus statement
Bibliographic record
Abstract
Despite the increasing inclusion of communication skills in accreditation standards and an increase in time dedicated to teaching these skills, communication is often regarded as a separate skill and is therefore, not consistently represented in overall systems of assessment in Health Professions Education (HPE). The ascendence of competency-based medical education, programmatic assessment, artificial intelligence, and widespread use of telehealth, alongside changing patient expectations warrant an update in thinking about the assessment of communication skills in health professions education. This consensus statement draws on existing literature, expert pinion, and emerging challenges to situate the assessment of communication skills in the contemporary health professions education context. The statement builds on previous work to offer an update on the topic and include new developments related to assessment, particularly: the challenges and opportunities associated with systems of assessment; patient and peer perspectives in assessment; assessment of interprofessional communication, cross-cultural communication, digital communication; and assessment using digital technologies. Consensus was reached through extensive discussion among the authors and other experts in HPE, exploration of the literature, and discussion during an Ottawa 2024 conference workshop. The statement puts forward a summary of available evidence with suggestions for what educators and curriculum developers should consider in their planning and design of the assessment of communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.112 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".