Developing an Unguided Internet-Delivered Intervention for Emotional Distress in Primary Care Patients: Applying Common Factor and Person-Based Approaches
Bibliographic record
Abstract
BACKGROUND: Developing effective, unguided Internet interventions for mental health represents a challenge. Without structured human guidance, engagement with these interventions is often limited and the effectiveness reduced. If their effectiveness can be increased, they have great potential for broad, low-cost dissemination. Improving unguided Internet interventions for mental health requires a renewed focus on the proposed underlying mechanisms of symptom improvement and the involvement of target users from the outset. OBJECTIVE: The aim of our study was to develop an unguided e-mental health intervention for distress in primary care patients, drawing on meta-theory of psychotherapeutic change and utilizing the person-based approach (PBA) to guide iterative qualitative piloting with patients. METHODS: Common factors meta-theory informed the selection and structure of therapeutic content, enabling flexibility whilst retaining the proposed necessary ingredients for effectiveness. A logic model was designed outlining intervention components and proposed mechanisms underlying improvement. The PBA provided a framework for systematically incorporating target-user perspective into the intervention development. Primary care patients (N=20) who had consulted with emotional distress in the last 12 months took part in exploratory qualitative interviews, and a subsample (n=13) undertook think-aloud interviews with a prototype of the intervention. RESULTS: A flexible intervention was developed, to be used as and when patients need, diverting from a more traditional, linear approach. Based on the in-depth qualitative findings, disorder terms such as "depression" were avoided, and discussions of psychological symptoms were placed in the context of stressful life events. Think-aloud interviews showed that patients were positive about the design and structure of the intervention. On the basis of patient feedback, modifications were made to increase immediate access to all therapeutic techniques. CONCLUSIONS: Detailing theoretical assumptions underlying Internet interventions for mental health, and integrating this approach with systematic in-depth qualitative research with target patients is important. These strategies may provide novel ways for addressing the challenges of unguided delivery. The resulting intervention, Healthy Paths, will be evaluated in primary care-based randomized controlled trials, and deployed as a massive open online intervention (MOOI).
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".