Uptake of a self‐guided digital treatment for depression and anxiety: A qualitative study exploring patient perspectives and decision‐making
Bibliographic record
Abstract
BACKGROUND: Despite the demonstrated efficacy and potential scalability of self-guided digital treatments for common mental health conditions, there is substantial variability in their uptake and engagement. This study explored the decision-making processes, influences and support needs of people taking up a self-guided digital treatment for anxiety and/or depression. METHODS: Australian-based adults (n = 20) were purposively sampled from a trial of self-guided digital mental health treatment. One-to-one, semistructured interviews were conducted, based on the Ottawa Decision-Support Framework. Interviews were transcribed verbatim and analysed thematically using framework methods. Baseline sociodemographic, clinical and decision-making characteristics were also collected. RESULTS: Analyses yielded four themes. Theme 1 captured participants' openness to try self-guided digital treatment, despite limited deliberation on potential downsides or alternative options. Theme 2 highlighted that immediacy and ease of access were major drivers of uptake, which participants contrasted with gaps in access and continuity of care in face-to-face services, especially rurally. Theme 3 centred on participants as the main agents in their decision-making, with family and health professional attitudes also reportedly influencing decision-making. Theme 4 revealed participants' primary motivations for deciding to take up treatment (e.g., the potential to increase insight and coping skills), while also acknowledging that pre-existing characteristics (e.g., health and digital literacy, insight) determined participants' personal suitability for self-guided digital treatment. CONCLUSION: Findings help to elucidate the decision-making influences and processes amongst people who started a self-guided treatment for depression and anxiety. Additional information and decision support resources appear warranted, which may also improve the accessibility of self-guided treatments. PUBLIC OR PATIENT CONTRIBUTION: Patients were interviewed about their views and experiences of decision-making about accessing and taking up treatment. As such, patient contribution to the research was as study participants.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".