Mining the Gems of a Web-Based Mindfulness Intervention: Qualitative Analysis of Factors Aiding Completion and Implementation
Bibliographic record
Abstract
BACKGROUND: Digital health interventions provide a cost effective and accessible means for positive behavior change. However, high participant attrition is common and facilitators for implementation of behaviors are not well understood. OBJECTIVE: The goal of the research was to identify elements of a digital mindfulness course that aided in course completion and implementation of teachings. METHODS: Inductive thematic analysis was used to assess participant comments regarding positive aspects of the online mindfulness course Mindfulness for Well-being and Peak Performance. Participants were aged 18 years and older who had self-selected to register and voluntarily completed at least 90% the course. The course comprised educator-guided lessons and discussion forums for participant reflection and feedback. Participant comments from the final discussion forum were analyzed to identify common themes pertaining to elements of the course that aided in course completion and implementation of teachings. RESULTS: Of 3355 course completers, 283 participants provided comments related to the research question. Key themes were (1) benefits from the virtual community, (2) appeal of content, (3) enablers to participation and implementation, and (4) benefits noted in oneself. Of subthemes identified, some, such as community support, variety of easily implementable content, and free content access, align with that reported previously in the literature, while other subthemes, including growing together, repeating the course, evidence-based teaching, and immediate benefits on physical and mental well-being, were novel findings. CONCLUSIONS: Themes identified as key elements for aiding participant completion of a mindfulness digital health intervention and the implementation of teachings may inform the effective design of future digital health interventions to drive positive health behaviors. Future research should focus on understanding motivations for participation, identification of effective methods for participant retention, and behavior change techniques to motivate long-term adherence to healthy behaviors.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.017 | 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".