Web-Based Intervention in Mindfulness Meditation for Reducing Residual Depressive Symptoms and Relapse Prophylaxis: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mindful Mood Balance (MMB) is a Web-based intervention designed to treat residual depressive symptoms and prevent relapse. MMB was designed to deliver the core concepts of mindfulness-based cognitive therapy (MBCT), a group treatment, which, despite its strong evidence base, faces a number of dissemination challenges. OBJECTIVE: The present study is a qualitative investigation of participants' experiences with MMB. METHODS: Qualitative content analysis was conducted via 38 exit interviews with MMB participants. Study inclusion required a current PHQ-9 (Patient Health Questionnaire) score ≤12 and lifetime history ≥1 major depressive episode. Feedback was obtained on specific website components, program content, and administration as well as skills learned. RESULTS: Codes were assigned to interview responses and organized into four main themes: MBCT Web content, MBCT Web-based group process, home practice, and evidence of concept comprehension. Within these four areas, participants highlighted the advantages and obstacles of translating and delivering MBCT in a Web-based format. Adding increased support was suggested for troubleshooting session content as well as managing time challenges for completing home mindfulness practice. Participants endorsed developing affect regulation skills and identified several advantages to Web-based delivery including flexibility, reduced cost, and time commitment. CONCLUSIONS: These findings support the viability of providing MBCT online and are consistent with prior qualitative accounts derived from in-person MBCT groups. While there is certainly room for innovation in the domains of program support and engagement, the high levels of participant satisfaction indicated that MMB can significantly increase access to evidence-based psychological treatments for sub-threshold symptoms of unipolar affective disorder.
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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 | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".