Efficacy of a brief mindfulness-based intervention for brain tumor survivors experiencing depressive symptoms: a pilot study
Bibliographic record
Abstract
Objectives: We tested the feasibility and efficacy of a brief mindfulness-based intervention (bMBI) for adult brain tumor (BT) survivors experiencing elevated depressive symptoms. Specifically, we examined whether there are improvements in mental health symptoms (i.e. depressive symptoms, quality of life (QOL), mental wellbeing, perceived stress) among participants at post-intervention.Methods: Nineteen BT survivors participated in a five-session, in-person, group-based bMBI intervention. Data on attendance, depressive symptoms, QOL, mental well-being, and perceived stress were collected at three timepoints. Findings: The preliminary analysis supports that this bMBI: (a) was efficacious in decreasing depressive symptoms and perceived stress, as well as increasing mental well-being and QOL, and (b) was feasible, with a retention rate greater than 70% and an attendance rate greater than 80% for adult BT survivors.Conclusions: These findings lend support for a feasible and efficacious intervention for adult BT survivors experiencing elevated depressive symptomsImplications: With the paucity of psychosocial interventions targeting adult BT survivorship, future empirically rigorous studies for this population are warranted. The uptake/adoption of this bMBI by psychosocial providers may play an important role in improving the mental health and QOL of adult BT survivors.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".