Therapeutic Misconception in Psychiatry Research: A Systematic Review
Bibliographic record
Abstract
Therapeutic misconception (TM) denotes the phenomenon in which research subjects conflate research purpose, protocols and procedures with clinical treatment. We examined the prevalence, contributory factors, clinical associations, impact, and collated solutions on TM within psychiatric research, and made suggestions going ahead. Literature search for relevant empirical research papers was conducted until February 2015. Eighty-eight reports were extracted, of which 31 were selected, summarised into different headings for discussion of implications and collated solutions of TM. We found variable and high rates of TM (ranging from 12.5% to 86%) in some psychiatry research populations. Contributory factors to TM included perceived medical roles of researchers, media, research setting and subject factors. Greater TM in affective, neurodevelopmental and psychotic spectrum conditions were associated with demographic variables (such as lower education, increased age), clinical factors (such as poor insight, cognitive deficits, increased symptoms, poorer self-rated quality of health), and social functioning (such as decreased independence). Inattention to TM may lead to frustration, negative impression and abandonment of participation in psychiatry research. Strategies such as the employment of a neutral educator during the informed consent process and education modules may be effective in addressing TM. Further research is warranted to examine the different TM facets, specific clinical correlates and more effective management strategies.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".