Patients’ reasons for declining a primary care trial online therapy: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Integrating therapist-led sessions and cognitive behavioural therapy (CBT) materials within one online platform may be effective for people with depression. A trial evaluating this mode of delivering CBT is being conducted. To maximise future trial recruitment and understand patients' views of health interventions, it is important to explore reasons for declining to participate. AIM: To explore patients' reasons for declining to participate in a trial of integrated online CBT for depression. DESIGN & SETTING: A mixed-methods study collecting data from patients via questionnaires and telephone interviews at three UK trial sites. METHOD: Individuals completed a short questionnaire about their reasons for not taking part in the trial. Telephone interviews further explored these reasons with a subgroup. Quantitative data were summarised using descriptive statistics. Qualitative interviews were analysed thematically. RESULTS: = 262). Qualitative interviews with 15 'decliners' highlighted that decisions related to perceptions of eligibility, previous experiences of CBT, and uncertainty about receiving CBT online. Personal circumstances, depressive symptoms, or other mental health issues were also barriers to participation. CONCLUSION: Reasons given by primary care patients for not taking part in a trial of integrated online CBT suggest that, at the point of recruitment, it is important to discuss the patient's perceptions of their eligibility and whether they would accept the intervention being evaluated.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".