Evaluation of the e–Mental Health Intervention Make It Training From Patients' Perspectives: Qualitative Analysis Within the Reduct Trial
Bibliographic record
Abstract
BACKGROUND: Make It Training is an e-mental health intervention designed for individuals with cancer that aims to reduce psychological distress and improve disease-related coping and quality of life. OBJECTIVE: This study evaluated the experienced usefulness and usability of the web-based Make It Training intervention using a qualitative approach. METHODS: In this study, semistructured interviews were conducted with participants at different cancer stages and with different cancer entities. All participants had previously taken part in the Reduct trial, a randomized controlled trial that assessed the efficacy of the Make It Training intervention. The data were coded deductively by 2 independent researchers and analyzed iteratively using thematic codebook analysis. RESULTS: Analysis of experienced usefulness resulted in 4 themes (developing coping strategies to reduce psychological distress, improvement in quality of life, Make It Training vs traditional psychotherapy, and integration into daily life) with 11 subthemes. Analysis of experienced usability resulted in 3 themes (efficiency and accessibility, user-friendliness, and recommendations to design the Make It Training intervention to be more appealing) with 6 subthemes. Make It Training was evaluated as a user-friendly intervention helpful for developing functional coping strategies to reduce psychological distress and improve quality of life. The consensus regarding Make It Training was that it was described as a daily companion that integrates well into daily life and that it has the potential to be routinely implemented within oncological health care either as a stand-alone intervention or in addition to psychotherapy. CONCLUSIONS: e-Mental health interventions such as Make It Training can target both the prevention of mental health issues and health promotion. Moreover, they offer a cost-efficient and low-threshold option to receive psycho-oncological support.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".