Mixed-methods single-arm repeated measures study evaluating the feasibility of a web-based intervention to support family carers of persons with dementia in long-term care facilities
Bibliographic record
Abstract
Following institutionalization of a relative with Alzheimer disease and related dementias (ADRD), family carers continue to provide care. They must learn to negotiate with staff and navigate the system all of which can affect their mental health. A web-based intervention, My Tools 4 Care-In Care (MT4C-In Care) was developed by the research team to aid carers through the transitions experienced when their relative/friend with ADRD resides in a long-term care (LTC) facility. The purpose of this study was to evaluate MT4C-In Care for feasibility, acceptability, ease of use, and satisfaction, along with its potential to help decrease carer’s feelings of grief and improve their hope, general self-efficacy, and health-related quality of life. The study was a mixed-methods single-arm repeated measures feasibility study. Participants accessed MT4C-In Care over a 2-month period. Data were collected at baseline and 1 and 2 months. Using a checklist, participants evaluated MT4C-In Care for ease of use, feasibility, acceptability, and satisfaction. Measures were also used to assess the effectiveness of the MT4C-In Care in improving hope (Herth Hope Index), general self-efficacy (GSES), loss and grief (NDRGEI), and health-related quality of life (SF12v2) of participants. Qualitative data were collected at 2 months and informed quantitative findings. The majority of the 37 participants were female (65%; 24/37), married (73%; 27/37), and had a mean age of 63.24 years (SD = 11.68). Participants reported that MT4C-In Care was easy to use, feasible, and acceptable. Repeated measures ANOVA identified a statistically significant increase over time in participants hope scores (p = 0.03) and a significant decrease in grief (< 0.001). Although significant differences in mental health were not detected, hope (r = 0.43, p = 0.03) and grief (r = − 0.66, p < 0.001) were significantly related to mental health quality of life. MT4C-In Care is feasible, acceptable, and easy to use and shows promise to help carers of family members with ADRD residing in LTC increase their hope and decrease their grief. This study provides the foundation for a future pragmatic trial to determine the efficacy of MT4C-In Care. ClinicalTrials.gov NCT03571165. June 30, 2018 (retrospectively registered).
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.002 |
| 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".