Development and initial validation of the Therapeutic Misunderstanding Scale for use with clinical trials research participants
Bibliographic record
Abstract
Therapeutic misconception is evident when clinical trials participants conflate research and treatment, erroneously believing that every aspect of the research is intended to be for their direct benefit. We developed the 20-item Therapeutic Misunderstanding Scale (TMU) based on responses from 464 community-dwelling adults 50+ years of age (Study 1). A three-factor solution based on Horng and Grady's (2003) three-facets definition was identified using both exploratory and confirmatory factor analyses (EFA and CFA; these analyses were performed on separate samples). CFA results point to a second-order solution where each of Horng and Grady's three facets contribute significantly to the measurement of a higher-order therapeutic misunderstanding latent construct. Internal consistency of TMU responses (full scale) as well as the therapeutic misconception, misestimation, and optimism subscales were calculated as α = 0.88, α = 0.83, α = 0.79, and α = 0.75, respectively. These results were subsequently supported with responses from former clinical trials participants (Study 2). This TMU provides applied researchers a brief measure for use in future studies as well as a screening instrument for clinicians to more fully assess informed consent for participation in clinical trials research.
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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 | 0.072 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".