The Privileged Status of Prestigious Terminology: Impact of ???Medicalese??? on Clinical Judgments
Bibliographic record
Abstract
PURPOSE: Health professionals frequently use medical terminology like dyspnea or nasopharyngitis. These two studies examine how the use of medical terms affects the judgments of seriousness, prevalence, and disease; and diagnostic judgments. METHOD: In study 1, a survey containing the names of 22 diseases with either a medical or lay description was completed by 47 undergraduate psychology students and 25 medical students, who were asked to judge seriousness, prevalence, and how "disease-like" it was. In study 2, undergraduate students learned four "pseudopsychiatry" conditions, each with four associated features. Features were presented in lay or medical versions. They were then tested with 18 new cases with two medical features from one condition and two lay terms from the other. RESULTS: In study 1, the medical students rated conditions as more disease-like, more serious, and less prevalent than did the psychology students. Medical descriptions were seen as significantly less common and somewhat more serious and more disease-like. In study 2, the participants rated the condition with medical features consistently more likely than the alternative, regardless of training condition. CONCLUSIONS: The specific words used to describe a feature or condition can have an impact on judgments of likelihood of disease, and, to a lesser extent, judgments of seriousness.
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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 | 0.014 | 0.204 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".