Acceptance and Commitment Training for Family Caregivers of People with Neurodevelopmental Disabilities: Protocol for a Collaborative Implementation Study
Bibliographic record
Abstract
BACKGROUND: Family caregivers of individuals with neurodevelopmental disabilities (NDDs) often experience stress, anxiety, and depression; however, few evidence-based interventions are designed to improve their mental well-being. To address this gap, we developed an acceptance and commitment training (ACT) group-based workshop cofacilitated by trained caregivers and clinicians (Caring for the Caregiver Acceptance and Commitment Training [CC-ACT]). OBJECTIVE: This study evaluates the real-world implementation of this innovative, evidence-based ACT workshop aimed at enhancing caregiver mental health and resilience. METHODS: Guided by the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) implementation science framework, this study examines the workshop across these 5 domains. We delivered the CC-ACT workshops virtually or in-person across 11 intervention sites in Canada, including hospital and community agencies that provide services to children with NDDs and their families. Family caregivers (ie, a parent, grandparent, or adult sibling) of someone with an NDD were eligible to participate in the workshops, with site-specific criteria set by each host agency. Caregivers participated in preintervention, postintervention, and 3-month follow-up assessments measuring stress, resilience, and self-compassion using validated instruments (21-item Depression, Anxiety and Stress Scale; Parenting Stress Index, 4th Edition; Brief Family Distress Scale; Multi-System Model of Resilience Inventory; and Self-Compassion Scale-Short Form), alongside ACT process measures (Cognitive Fusion Questionnaire, Valued Living Questionnaire, and Acceptance and Action Questionnaire-II). Implementation fidelity was assessed through checklists and surveys. Focus groups with caregiver facilitators, clinician facilitators, workshop participants, and organizational leaders were held to qualitatively evaluate the implementation process and the caregiver-clinician cofacilitation model. Qualitative data will be analyzed using descriptive content analysis, a flexible approach that can be used to systematically summarize different types of qualitative data. Quantitative data will be analyzed through repeated measures ANOVA and mixed-effects modeling, with subgroup analyses and multiple imputation for missing data. RESULTS: The CC-ACT workshops successfully reached 195 caregivers of individuals with NDDs. Two focus groups that included 5 caregiver workshop participants, 13 facilitators, and 5 organizational leaders were conducted. We anticipate that the workshops will demonstrate positive impacts on caregiver well-being, with variability in effectiveness based on participant characteristics and real-world implementation contexts. The findings are expected to identify key predictors of outcomes, equity and access barriers, and best practices for scaling and sustaining high-fidelity, adaptable caregiver interventions across diverse Canadian settings. Funding began in January 2022, data collection was completed in 2024, and data analyses will be completed by the end of 2025. CONCLUSIONS: The CC-ACT workshop is a promising approach to enhancing the mental well-being of caregivers of individuals with NDDs. The RE-AIM framework helps capture process data systematically, documenting the balance between fidelity and adaptation. The study findings should support the refinement of implementation strategies and support the broader scalability of the intervention to diverse community settings. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75049.
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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 | 0.075 | 0.049 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.046 | 0.009 |
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".