Evaluation of the Acceptability and Utility of a Decision Aid for the Treatment of Adult Depression
Bibliographic record
Abstract
Decision aids communicate the best available evidence on treatment options to patients in order to facilitate informed decision-making. Research suggests that decision aids improve patients’ treatment knowledge, reduce decisional conflict, and promote more active decision-making. Despite evidence of the utility of decision aids in physical health conditions, they are both understudied and rarely used for mental health problems. The present study evaluated the acceptability and utility of a decision aid for the treatment of depression and its relationship to participants' knowledge and decision-making. Undergraduate students ( Participants completed a follow-up knowledge test, along with a series of questionnaires assessing acceptability of the decision aid and other variables of interest (e.g., decisional conflict, preparation for decision-making). One month later, participants completed the knowledge test for the third time. Overall, a majority of participants rated the decision aid as highly acceptable and useful. There was a significant increase in participants’ knowledge of depression treatment from prior to reading to after reading the decision aid. Although participants’ knowledge scores decreased slightly at the 1-month follow-up, they were still significantly higher than their baseline scores. The hypothesis that participants’ treatment choice would be influenced by the order in which treatment options were presented to them within the decision aid was partially supported. However, this effect was eliminated when the few participants who selected the “no treatment” option were excluded, as well as when participants were given the additional option of selecting a combined treatment (i.e., medication and psychotherapy). This is one of the few studies aimed at expanding the use of decision aids to mental health conditions. Future research should evaluate the utility of this decision aid with a clinical sample. Additionally, the methodology used in this study can be translated to the evaluation of other decision aids.
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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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".