A Generalization of the «Lady-Tasting-Tea» Procedure to Link Qualitative and Quantitative Approaches in Psychiatric Research
Bibliographic record
Abstract
In Fisher’s “The Design of Experiments”, a trial was designed to test a lady’s claim to be able to discriminate whether the milk or the tea was added first to a cup. In this trial, eight cups are poured, four with milk first and four with tea first. They are then presented in random order to a subject who has to divide them into two sets of 4, according his/her belief about the "treatment" received. The present paper generalizes this design so that a hypothesis concerning the existence of two sub groups in a set of psychiatric patient records (whether written, audiotaped or videotaped) can be tested rigorously from a statistical point of view. Tables are proposed to enable power and sample size calculations. A real example is presented; it shows that psycho-dynamically oriented professionals are able to discriminate seven healthy adults who have experienced a sibling’s cancer during childhood or adolescence from seven matched controls. This method is particularly suited to small sample studies that explore elusive clinical hypotheses traditionally tackled with qualitative methodologies.
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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 | 0.166 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".