The impacts of dance/movement therapy on dementia caregivers’ burden and resilience: A mixed-methods exploration
Bibliographic record
Abstract
Many interventions have been explored which aim to support dementia caregivers. However, the traditional approaches focus on cognitive strategies to reduce negative psychological outcomes (e.g., burden). Presently, there is a lack of strengths-based programs designed to enhance positive aspects of caregiver health. Although the benefits of dance/movement therapy (DMT) have been shown with other populations, it is underexplored for caregivers apart from their loved one with dementia. For the current study, ten women participated in the first DMT program for dementia caregivers, which was evaluated with a convergent mixed-methods design. Caregiver burden and resilience was assessed at baseline and follow-up, and therapeutic factors of DMT were assessed at three time points immediately following DMT (week 3, 4, and 5). Quantitative analyses showed that caregiving burden was significantly reduced, and resilience did increase, but not significantly. Qualitative findings from journal entries and semi-structured interviews revealed that DMT enhanced coping through bolstering resilience factors such as self-efficacy and cognitive reframing. Together, these findings suggest that DMT shows promise as an intervention which can meaningfully decrease burden and enhance resilience factors for caregivers. More research is needed which measures outcomes and mechanisms of DMT for caregivers in the context of dementia care. • This study describes a novel DMT intervention for informal dementia caregivers. • The impacts of the intervention were explored with a mixed-methods approach. • Participants’ caregiving burden significantly decreased after the DMT program. • Qualitative findings revealed that DMT enhanced coping and resilience factors.
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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 | 0.054 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".