Treatment preferences and self-stigma in depression: Development and validation of the brief ATDT-SF
Bibliographic record
Abstract
BACKGROUND: Patients' beliefs about depression and different antidepressant treatment options may influence help-seeking behaviour, treatment adherence, and ultimately clinical outcomes. The Attitudes Toward Depression and its Treatment (ATDT) questionnaire was developed to assess these attitudes and beliefs; however, subsequent research revealed limitations in its psychometric properties. We sought to develop and validate a shortened version (ATDT-SF) with improved reliability. METHODS: We used data from 321 patients with first-episode depression initiating treatment enrolled in the BrainDrugs-Depression cohort (age 18-65, 71 % female). We randomly divided the sample into development (n = 209) and validation (n = 112) subsets. Exploratory factor analysis identified a parsimonious factor structure, which was confirmed using confirmatory factor analysis. We assessed associations between the ATDT-SF factors and clinical variables, and compared attitudes across Danish, Canadian, and Australian samples. RESULTS: A 13-item, four-factor model demonstrated an acceptable fit and resulted in the factors: negative attitudes toward antidepressants, help-seeking from medical professionals, self-stigma, and preference for psychotherapy. Age was positively associated with negative attitudes toward antidepressants (p = 0.004), while depression severity showed a significant positive association with experienced stigma (p = 0.004). Patients reported significantly stronger help-seeking from medical professionals and higher self-stigma compared to those from Canadian and Australian samples, while negative attitudes toward antidepressants were similar across countries. CONCLUSIONS: The ATDT-SF provides a reliable measure of attitudes toward depression and its treatment and self-stigma, which may be important contextual factors in treatment planning and depression management.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".