The Impact of Advice Seekers’ Need Salience and Doctors’ Communication Style on Attitude and Decision Making: A Web-Based Mammography Consultation Role Play
Bibliographic record
Abstract
BACKGROUND: Patients and advice seekers come to a medical consultation with typical needs, and physicians require adequate communication skills in order to address those needs effectively. It is largely unclear, however, to what extent advice seekers' attitudes toward a medical procedure or their resulting decisions are influenced by a physician's communication that ignores or explicitly takes these needs into account. OBJECTIVE: This experimental study tested how advice seekers' salient needs and doctor's communication styles influenced advice seekers' attitudes toward mammography screening and their decision whether or not to participate in this procedure. METHODS: One hundred women (age range 20-47 years, mean 25.22, SD 4.71) participated in an interactive role play of an online consultation. During the consultation, a fictitious, program-controlled physician provided information about advantages and disadvantages of mammography screening. The physician either merely communicated factual medical information or made additional comments using a communication style oriented toward advice seekers' typical needs for clarity and well-being. Orthogonal to this experimental treatment, participants' personal needs for clarity and for well-being were either made salient before or after the consultation with a needs questionnaire. We also measured all participants' attitudes toward mammography screening and their hypothetical decisions whether or not to participate before and after the experiment. RESULTS: =.046). Those participants whose needs were made salient before the consultation responded to the physician's communication style, while participants without salient needs did not. When the physician used a need-oriented communication style, those participants with salient needs had a more positive attitude toward mammography after the consultation than before (mean 0.13, SD 0.54), while they changed their attitude in a negative direction when confronted with a purely fact-oriented communication style (mean -0.35, SD 0.80). The same applied to decision modification (need-oriented: mean 0.10, SD 0.99; fact-oriented: mean -0.30, SD 0.88). CONCLUSIONS: The findings underline the importance of communicating in a need-oriented style with patients and advice seekers who are aware of their personal needs. Ignoring the needs of those people appears to be particularly problematic. So physicians' sensitivity for advice seekers' currently relevant needs is essential.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".