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Record W4294316272 · doi:10.2196/37406

Mining the Gems of a Web-Based Mindfulness Intervention: Qualitative Analysis of Factors Aiding Completion and Implementation

2022· article· en· W4294316272 on OpenAlexvenueno aff
Muskan Yadav, Sandra Neate, Craig Hassed, Richard Chambers, Sherelle Connaughton, Nupur Nag

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMindfulnessThematic analysisPsychologyPsychological interventionIntervention (counseling)Medical educationQualitative researchContent analysisDigital healthMental healthAttritionApplied psychologyPsychotherapistHealth careMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.000

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.

Opus teacher head0.157
GPT teacher head0.529
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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