A grounded theory of how people process their cancer experiences through a mindfulness-based expressive arts group
Bibliographic record
Abstract
Abstract Background: Given the distress associated with cancer experiences, there is a growing interest in mindfulness-based expressive arts interventions (MBAIs) for promoting patients' well-being. Our research objective was to develop a theoretical understanding of how patients with cancer experience, use, and draw meaning from an MBAI. Methods: We used a constructivist grounded theory research design and gathered narrative descriptions of participants' (N = 32) MBAI experiences through semistructured interviews and field notes. Participants brought the artwork they had created in the group, facilitating art elicitation. Data were analyzed with grounded theory methods. Results: Participants described how the dynamic interplay of mindfulness, the arts, group sharing, and bearing witness facilitated the processing and sharing of hidden thoughts, experiences, and emotions. The group facilitated several unique meaning-making processes, including re-envisioning personal identity within disruption and loss, creating a fitting container for the exploration of diverse emotions, revisiting difficult experiences within the sensitivity of art, and visualizing hope and healing. This process resulted in important learnings and benefits for living in the here and now: relational connections, facing cancer through artistic play, discovering intuition and personal resources, learning an emotional language and a new mindset to move forward, understanding what one needs to heal, and fostering gratitude and hope. Conclusions: MBAIs allowed for a multimodal form of meaning making which facilitated coping, adjustment, and living well with cancer. These findings will enable practitioners to design and implement more effective health services and inform future research about this therapeutically promising approach to psychosocial oncology care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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 source (direct Gemma or distilled Codex), 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".