Creating case scenarios or vignettes using factorial study design methods
Bibliographic record
Abstract
AIM: This paper is a report of a study conducted to develop clinical case vignettes using an adaptation of an incomplete factorial study design methodology. BACKGROUND: In health care, vignettes or cases scenarios are core to problem-based learning, common in practice guideline development processes, and increasingly being used in patient or care-giver studies of chronic or life-threatening illnesses. A large number of behavioural, psycho-social and clinical factors can be relevant in such decision problems. Unbiased methods for choosing what factors to include are needed, when it is not possible to include all relevant combinations of factors in the vignettes. METHOD: The factors to be considered, number of levels or categories for each factor, and desired number of scenarios were decided in advance. An algorithm was used first to create the full factorial data set, and then a random subset of combinations was generated, according to predefined criteria, based on maximizing determinants. The subset of combinations was incorporated into written vignettes. The study was conducted in 2004-2005. FINDINGS: Application of the method yielded diverse and balanced scenarios that covered the full range of factors to be considered for a project to elicit health providers' processes in diet counselling for dyslipidemia. CONCLUSION: The approach is flexible, decreases possible researcher bias in the creation of vignettes, and can improve statistical power in survey research. This novel application of study design methodology merits consideration when vignettes are being developed to elicit opinions or decisions in studies of complex health issues.
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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 | 0.066 | 0.124 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".