Engaging care partners of persons living with dementia in acceptance and commitment therapy (ACT) programs: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Acceptance and commitment therapy (ACT) is a relatively new type of psychotherapy effective for treating depression and anxiety amongst family care partners of persons living with dementia [PLWD]. However, care partner engagement in mental health services is low and specific guidelines for designing ACT programs for care partners of PLWD do not exist. The purpose of this scoping review was to examine patterns in care partner engagement in ACT programs to identify program factors potentially influencing engagement. METHODS: A comprehensive scoping review according to Arksey and O'Malley's framework was followed. Databases and grey literature were searched for primary studies of ACT programs with care partners of PLWD. Data were charted and synthesized. RESULTS: Ten studies met inclusion criteria and were analyzed. Amongst these, engagement was highest in three ACT programs that were delivered individually, remotely and were therapist-led or supported. Conversely, engagement was the lowest in two ACT programs that were self-directed, web-based and had minimal or no care partner-therapist interaction. Program factors perceived as influencing engagement included tailoring and personalization, mode of delivery and format, therapeutic support and connectedness, program duration and pace. CONCLUSION: Findings from this review suggest that care partners engagement may be promoted by designing ACT programs that focus on the therapeutic client-therapist relationship, are delivered remotely and individually. Future research should focus on evaluation of best implementation practices for engagement and effectiveness.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".