Exploration of Factors That Affect Engagement With the Experience Sampling Method and Service Users’ Experience of This Within the AVATAR2 Trial: Mixed Methods Study
Bibliographic record
Abstract
Background: Experience sampling methodology (ESM) is an assessment method used in psychosis research. Symptom severity and gender may be associated with ESM engagement. Exploring qualitative experiences of using ESM among people with psychosis should aid developing more relevant, accessible digital assessments. Objective: This study aimed to examine factors that could affect engagement with ESM, such as associations of completion rates with age, ethnicity, gender, and clinical severity. It also aimed to explore qualitatively service users' experiences of using this data collection method. Methods: Data from 134/207 AVATAR2 trial (ISRCTN55682735) participants were used to evaluate associations between demographic variables, symptom severity, and ESM completion rates. Trial participants were purposively sampled to participate in an interview to discuss their experiences of using ESM or to discuss reasons why they chose not to use it. Results: Multiple regression analyses of 134 participants found that age, gender, ethnicity, and clinical severity were not associated with ESM completion rates (F5,128=0.548; P=.74). A thematic analysis of 17 participant interviews found 3 overarching themes: Factors affecting engagement with ESM, Perceived benefits of ESM, and Suggestions for improvement. These themes described how ESM has multiple benefits for people with psychosis, including increasing knowledge and awareness of mental health. ESM was straightforward and easy to use; however, engaging in other activities, experiencing positive symptoms, little experience using technology, and trial involvement impacted engagement. Participant's decision to use ESM could be influenced by concerns about security and privacy. Conclusions: Recommendations are made on how engagement with ESM can be improved, making it easier to use this method with this population, including providing increased support or training when using digital-based assessment or intervention as well as providing information on how digital data are used and recorded.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".