Family Camp: A multi-disciplinary intervention for brain tumor patients and families.
Bibliographic record
Abstract
240 Background: The diagnosis of a brain tumor is a catastrophic life changing event that impacts the entire family. Patients and caregivers experience dramatic role changes, concern for their children, financial stress, and isolation. For many, the situation is overwhelming. A novel intervention to address these needs is “Family Camp”. Our vision for camp was to provide respite to the entire family, decrease caregiver stress, improve family connections and promote a sense of understanding and connection with the community at large. Methods: In a unique partnership with the widow of a former patient, the members of the UCSF Neuro-Oncology Division created and offered a weekend camp for brain tumor patients with children. Team members included MDs, RNs, social workers, psychologists, artists, body workers, “camp counselors” and community volunteers. In addition to respite, camp was structured through art, songs, projects and games to deliver opportunities for understanding and community building, including understanding the disease and its impact on the family. Therapeutic interventions included couples activities, counseling, parenting strategies, and providing a sense of connection to others dealing with brain tumors, the health care team, and camp volunteers. Anxiety, stress, depression, coping and values based living were measured pre and post camp using DASS21, CES-D, Brief Cope, Values Based Living instruments, and survey questions. Results: 11 families attended camp for 3 days in 2014. Improvement was noted in post camp testing of depression, anxiety and stress versus pre-camp. Survey questions showed the most common and important outcome to be connection, specifically that patients, caregivers, and children made connections with similar others. Families reported being able to relax and felt taken care of. They liked the opportunity for undistracted family time, getting to know their health providers outside of the office, and couples benefited from family counseling. Conclusions: Capitalizing on the unique skills of a multi-disciplinary team, one that includes the patient’s health care team, can lead to the delivery of a novel intervention that improves the illness experience of brain tumor patients and families.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".