Evaluation of communication skills among physicians: A systematic review of existing assessment tools
Bibliographic record
Abstract
Abstract Background The importance of physician training in communication skills for behavior change counselling in the context of chronic disease management is increasingly recognized. However, little is known about the quality, utility and psychometric properties of existing communication assessment tools. Objective This study systematically reviewed existing assessment tools used to evaluate communication skills among physicians. Methods A systematic review was conducted in accordance with the PRISMA guidelines (CRD42018091932). Four databases (PUBMED, EMBASE, PsychINFO, SCOPUS) were searched up to December 2018, generating 3902 unique articles which were screened by two authors. A total of 57 articles met inclusion criteria and underwent full data extraction. Results Selected studies were published between 1990 and 2018. A total of 45 different assessment tools were identified. Only 47% of the studies mentioned any theoretical basis underlying the design of the tool. The most prevalent communication skills assessed were information giving (46%) or gathering (40%), eliciting patients’ perspective (44%) and agenda planning (37%). Forty-two percent were tool validation studies, but the majority (61%) reported on only one psychometric property. Study quality, using the modified COSMIN checklist, varied considerably, with an average score of 4.5/9 ± 1.3 representing moderate quality. Conclusions Despite identifying a high number of existing physician communication assessment tools, most were poorly validated and a high degree of heterogeneity in terms of skills assessed and study quality was observed. Most used in-person role-play exercises that are intrusive, expensive, and time-consuming, making them impractical for use within most medical contexts. Successful chronic disease management depends not only on feasible and effective communication skills training among physicians, but also on our ability to reliably assess skill acquisition. Key messages Training physicians’ behaviour change competencies is central to improving lifestyle changes among patients living with a chronic disease. Due to the major methodological shortcomings of existing communication assessment tools, we strongly recommend increased methodological rigor for the development of new tools.
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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 | 0.038 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".