Training patients to ask information verifying questions in medical interviews
Bibliographic record
Abstract
Purpose The main purpose of the paper was to examine whether a short patient training session on various ways of requesting physicians to clarify a piece of previously elicited information during medical consultation would improve information communication, thus increasing patient satisfaction. Design/methodology/approach A total of 114 adult patients voluntarily participated in the study which was carried out at a clinic in Canada. Half of the participants were randomly assigned to the experimental group and half to the control group. Males and females were evenly distributed in both experimental and control groups. Prior to their medical visits, participants in the experimental group received 10‐15‐minute face‐to‐face training, whereas the control group did not receive any training. The purpose of the training was to facilitate information transmission, with the intention to increase communication effectiveness and patient satisfaction. Immediately after their medical visits, all participants filled out a patient satisfaction questionnaire. Findings On all four dimensions of patient satisfaction (i.e. overall satisfaction, relationship satisfaction, communication satisfaction and expertise satisfaction), patients who received training scored significantly higher (were more satisfied) than patients who received no training. No consistent gender differences were found in patient satisfaction in both experimental and control groups. Research limitations/implications This study applied a psycholinguistics theory, conversational grounding, to the field of patient education and achieved positive results. Practical implications The success of the short training session provides health practitioners with a new method to help patients communicate more effectively, thus increasing satisfaction in medical interviews. Originality/value Focuses on a means to elicit information from patients in medical consultations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".