Development and Initial Evaluation of the Web-Based Self-Management Program “Partner in Balance” for Family Caregivers of People With Early Stage Dementia: An Exploratory Mixed-Methods Study
Bibliographic record
Abstract
BACKGROUND: People with dementia increasingly depend on informal caregivers. Internet-based self-management interventions hold considerable promise for meeting the educational and support needs of early stage dementia caregivers (EDCs) at a reduced cost. OBJECTIVE: This study aimed to (1) develop an online self-management program for EDC to increase self-efficacy and goal attainment, and (2) evaluate the program's feasibility and report preliminary data on effectiveness. METHODS: Based on the Medical Research Council (MRC) framework for the development and evaluation of complex interventions, a stepwise approach was adopted to explore potential user needs and develop and validate the content by means of (1) focus group discussions with dementia caregivers (N=28), (2) interviews with dementia care professionals (N=11), and (3) individual think-aloud usability tests with EDC (N=2) and experts (N=2). A pilot evaluation was conducted with EDC (N=17) to test the feasibility and establish preliminary effects. Self-report measures of feasibility were completed after the completion of intervention. Self-efficacy and goal attainment were evaluated before and after the intervention. RESULTS: The different steps provided useful information about the needs of potential users regarding the content and delivery of the program. This resulted in the newly developed "Partner in Balance" program. At the start, system failures resulted in a high noncompleter rate (7/17, 41%), but at the end, an acceptable feasibility score of 209 (range 54-234) was found. The convenience of completing the program at home, the tailored content, and the guidance (face-to-face and online) were appraised positively. Preliminary effects on caregiver self-efficacy (P<.05) and goal attainment (T>50) were promising. CONCLUSIONS: Adaptations were made to the program to limit the amount of system failures and prevent high noncompleter rates. As recommended by the MRC framework, confirming the feasibility and preliminary effectiveness is a valuable step toward examining the effectiveness of this newly developed intervention. TRIAL REGISTRATION: Dutch Trial Register (NTR): NTR4217; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4217 (Archived by WebCite at http://www.webcitation.org/6f6B8lvRP).
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".