Assessment of preferences for treatment: Validation of a measure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systematic measurement of treatment preferences is needed to obtain well-informed preferences. Guided by a conceptualization of treatment preferences, a measure was developed to assess treatment acceptability and preference. The purpose of this study was to evaluate the psychometric properties of the treatment acceptability and preferences (TAP) measure. The TAP measure contains a description of each treatment under evaluation, items to rate its acceptability, and questions about participants' preferred treatment option. The items measuring treatment acceptability were internally consistent (alpha > .80) and demonstrated validity, evidenced by a one-factor structure and differences in the scores between participants with preferences for particular interventions. The TAP measure has the potential for the assessment of acceptability and preferences for various behavioral interventions.
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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 | 0.002 | 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 | 0.000 | 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 it