Cancer-Related Search for Meaning Increases Willingness to Participate in Mindfulness-Based Stress Reduction
Bibliographic record
Abstract
OBJECTIVE: A cancer diagnosis can prompt an examination and reevaluation of life's meaning, purpose, and priorities. There is evidence that mindfulness-based stress reduction (MBSR) may help facilitate the meaning-making process. This study examined the influence of meaning in life on willingness to participate (WTP) in MBSR and identified factors associated with the search for and/or presence of meaning. METHODS: A cross-sectional survey study of 300 patients undergoing radiation therapy was conducted. WTP in MBSR was dichotomized into yes/no by asking, "Would you participate in an MBSR program if it was offered at the cancer center?" The search for, and the presence of, meaning were assessed using the Meaning in Life Questionnaire. RESULTS: Eighty patients (27%) indicated WTP in MBSR. In a multivariate logistic regression model, search for meaning was the only significant predictor of WTP in MBSR (adjusted odds ratio [AOR] =1.04, P ≤ .001, 95% CI = 1.01-1.08). Identifying as non-white (adjusted β = 4.62; 95% CI = 2.22-7.02; P < .001), and reporting subclinical (adjusted β = 3.59; 95% CI = 0.84-6.34; P = .01) or clinical levels (adjusted β = 5.52; 95% CI = 2.41-8.63; P = .001) of anxiety were the strongest predictors of search for meaning. CONCLUSION: Our study indicates that patients searching for meaning are receptive to MBSR. Nonwhite patients and those experiencing high levels of anxiety are most likely to endorse a search for meaning. Future research is needed to understand how best to support patients who are searching for meaning and remove barriers to evidence-based programs like MBSR.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".