Feasibility of Delivering an Avatar-Facilitated Life Review Intervention for Patients with Cancer
Bibliographic record
Abstract
Background: Life review, a narrative-based intervention, helps individuals organize memories into a meaningful whole, providing a balanced view of the past, present, and future. Examining how the content of memories contributes to life's meaning improves some clinical outcomes for oncology patients. Combining life review with other modalities may enhance therapeutic efficacy. We hypothesized a life review intervention might be enhanced when combined with a kinetic, digital representation (avatar) chosen by the patient. Our goal was to determine the feasibility of an avatar-based intervention for facilitating life review in patients with advanced cancer. Methods: We conducted an observational, feasibility trial in a supportive care clinic. Motion capture technology was used to synchronize voice and movements of the patient onto an avatar in a virtual environment. Semistructured life review questions were adapted to the stages of child, teenager, adult, and elder. Outcome measures included adherence, recruitment, comfort of study procedure, patients' perceived benefits, and ability to complete questionnaires, including the Edmonton Symptom Assessment System (ESAS) and Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp). Results: Seventeen patients were approached, with 11/12 completing the intervention. The total visit time of a single intervention averaged 67 minutes. The post-intervention survey found all patients agreed or strongly agreed (Likert Scale 1–5) they would participate again, would recommend it to others, and found the experience beneficial. After one month, ESAS scores were either unchanged or improved in 80% of patients. Conclusion: An avatar-facilitated life review was feasible with a high rate of adherence, completion, and acceptability by patients. The findings support the need for a clinical trial to test the efficacy of this novel intervention. Clinical Trial Number NCT03996642.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".