Design and cultural adaptation of an e-mental health intervention for depression
Bibliographic record
Abstract
This thesis employs mixed methodologies, including systematic review and meta-analysis, qualitative research and pre-post design empirical testing. It comprises the in-depth formative work for designing and testing the Step-by-Step e-mental health intervention for depression. We found that a Western only model of mental distress does not capture important local features relevant to informing the design and conduction of efficacy studies of interventions for common mental disorders. We also found that local adaption of self-help psychological interventions can contribute to increased intervention efficacy. Systematically carrying out cultural adaptation of Step-by-Step with local actors was crucial. The feasibility testing of Step-by-Step provided valuable information to inform the design of future large-scale efficacy testing and hinted that Step-by-Step is a potentially promising intervention for reducing the symptoms of depression. An adapted, minimally guided, internet-delivered intervention like Step-by-Step could help close the treatment gap for depression if integrated responsibly into an existing care system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".