Assessing and improving public mental health literacy concerning rTMS
Bibliographic record
Abstract
Abstract Background Repetitive transcranial magnetic stimulation (rTMS) has received empirical support as a viable treatment alternative for treatment-resistant major depressive disorder. Nevertheless, patients and the public-at-large may be hesitant to adopt rTMS. In three studies, we sought to (1) assess and (2) improve public perceptions of rTMS as a treatment for depression. Methods In Study 1 (N = 107), we administered questionnaires on Amazon’s Mechanical Turk (MTurk) to individuals from the US and Canada in a cross-sectional design to assess perceptions of rTMS compared to psychopharmacology, electroconvulsive therapy (ECT), and talk therapy. In Study 2 (N = 106), we again used an MTurk sample and a cross-sectional design to assess perceptions of rTMS after providing participants with a relatively long description of rTMS. In Study 3 (N = 308), we conducted an experiment in undergraduate students. Participants were randomized to one of four experimental conditions manipulating participants’ understanding of the causal mechanisms of depression prior to assessing their perceptions of rTMS. Results Public perceptions of rTMS were more negative than pharmacotherapy and talk therapy but not ECT (Study 1). rTMS perceptions were notably better when participants were given thorough information about rTMS procedures, pain, and side-effects (Study 2), compared to the previous study when they were given a very brief description of rTMS. Finally, perceptions of rTMS were significantly better when participants were given a brain circuitry-based causal explanation of depression compared to when they were given a psychological explanation of the causes of depression (Study 3). Conclusions Public perceptions of rTMS are relatively poor. To improve rTMS acceptability, practitioners should carefully consider patients’ prior attitudes and beliefs when explaining rTMS as a treatment alternative. Given that beliefs can have powerful effects on treatment outcome (e.g., placebo, nocebo), future research should explore whether rTMS effects on depression can be improved by facilitating less negative perceptions of rTMS.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".