Direct-to-consumer marketing of psychological treatments: A randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Although direct-to-consumer (DTC) marketing of pharmacologic interventions is effective and common, similar approaches have yet to be evaluated in the promotion of psychological treatments (PTs). This is the first randomized controlled trial evaluating the potential of DTC marketing of PTs. METHOD: Participants (N = 344; 75.0% female, mean age = 18.6 years, 48.5% non-Hispanic White) were randomly assigned to consume one of four extended commercial campaigns embedded within unrelated programming across 3 weeks. The four campaign conditions were a PT campaign, a PT informing about medication side effects campaign, a medication campaign, and a neutral campaign. Attitudes about and intention to seek psychological treatment were assessed prior to campaign exposure (T1), 1 week following the final week of campaign exposure (T2), and at a 3-month follow-up evaluation (T3). RESULTS: The percentage of participants who newly intended psychological treatment at T2 or T3 differed by condition, with those assigned to the PT campaign slightly more likely to have intended to receive psychological treatment at T2 or T3 than those in other conditions. Baseline reports of emotional symptoms moderated the effect of condition on attitudes toward PT and perceived likelihood of seeking treatment in the future. CONCLUSIONS: Findings support the preliminary utility of DTC marketing of psychological treatments. Increasing consumer knowledge of PTs may be a worthwhile complement to current dissemination and implementation efforts aimed at promoting the uptake of PTs in mental health care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".