Causal explanations of depression on perceptions of and likelihood to choose cognitive behavioural therapy and antidepressant medications as depression treatments
Bibliographic record
Abstract
OBJECTIVES: This research examined whether people's causal explanations of depression were associated with acceptability and efficacy-related treatment perceptions and likelihood to choose cognitive behavioural therapy (CBT) and antidepressant medication (ADM) as depression treatments. DESIGN: A cross-sectional internet-based design was used. METHODS: A general population sample was used over a clinical sample to study those who had not yet chosen to enter treatment. A total of 422 individuals were recruited through a crowdsourcing platform to complete an online survey. Measures included perceived causes of depression, perceived acceptability, efficacy and choice likelihood for ADM and CBT, and demographics. RESULTS: Those with biological causal explanations of depression were more favourable towards ADM on all three perceptual measures of acceptability, efficacy and likelihood to choose ADM as a treatment for depression. Personality/character-related causal explanations of depression were positively related to perceived efficacy and likelihood to choose CBT as a depression treatment. Those endorsing environmental stress causes of depression were more likely to choose CBT as a treatment for depression. CONCLUSIONS: Results indicated that people's beliefs about the causes of depression were related to their perceptions of and likelihoods to choose ADM and CBT as depression treatments. PRACTITIONER POINTS: Provides evidence of how different causal explanations of depression influence sufferers' likelihoods to choose ADM and CBT as possible treatments for their depression. Provides support for exploring potential patients' causal explanations about depression prior to recommending a treatment regimen.
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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 | 0.018 |
| 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) | 0.005 | 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".