Distinctiveness of disease prototypes in lay illness diagnosis: An exploratory observational study
Bibliographic record
Abstract
Research on disease prototypes has revealed that lay illness diagnosis is influenced by symptom typicality, suggesting that it represents a "prototype-matching process". This study further investigates the role of disease prototypes by examining associations of prototype distinctiveness with diagnostic expectancies. One hundred and eight lay participants rated the typicality of 30 symptoms for nine common physical diseases. On this basis, two structural features of individual disease prototypes were calculated: (a) distinctiveness, that is, the dissimilarity of a prototype relative to the others (Euclidean distance measure), and (b) richness, that is, the sum of symptoms' typicality. Moreover, prototype confidence and illness experience were assessed. Finally, as a proxy to diagnostic behaviour, diagnostic expectancies, that is, prospective beliefs actually to diagnose a disease when experiencing relevant symptoms, were measured. Multiple regression analyses revealed prototype confidence as strongest predictor of diagnostic expectancies. However, positive and partly significant associations were also found with prototype distinctiveness on all levels of data aggregation (disease-specific, disease-cluster-specific, and overall). Results are discussed as to their implications for studies in lay illness diagnosis and designing health education materials. Specifically, it is concluded that such materials should include symptomatic information not only as to symptoms' typicality, but also their distinctiveness, that is, information aiding in distinguishing different diseases.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".