Assessing the equivalency of face-to-face and online simulated patient interviews in an educational intervention
Bibliographic record
Abstract
BACKGROUND: In adapting to COVID-19, many health professional training programs moved abruptly from in-person to online simulated patient interviews for teaching and evaluation without the benefit of evidence regarding the efficacy of this mode of delivery. This paper reports on a multi-methods research project comparing in-person and online simulated patient interviews conducted by allied health professionals as part of an educational intervention offered at a large university teaching hospital. METHODS: Twenty-three participants conducted two 15-min interviews with simulated patients using previously validated scenarios of patients presenting with suicide risk. In order to assess the equivalency of the two modalities, physiological and psychological stress were measured using heart rate variability parameters and the State-Trait Anxiety Inventory respectively, and then were compared across cohorts using t-tests. Reflective interviews elicited qualitative impressions of the simulations that were subject to thematic qualitative analysis. RESULTS: There were no statistical differences in measures of psychological stress or physiological arousal of participant health care professionals who engaged with in-person versus online simulated interviews, suggesting they were equally effective in eliciting reactions commonly found in challenging clinical situations. In reflective interviews, participants commented on the realism of both modalities of simulated patient encounters and that simulated interviews provoked emotional and physiological responses consistent with actual patient encounters. CONCLUSIONS: These findings provide developing evidence that carefully designed online clinical simulations can be a useful tool for the education and assessment of healthcare professionals.
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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 | 0.052 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".