Study protocol: pragmatic randomized control trial of my tools 4 care- in care (MT4C-in care) a web-based tool for family Carers of persons with dementia residing in long term care
Bibliographic record
Abstract
BACKGROUND: When a family member resides in long term care facility (LTC), family carers continue caregiving and have been found to have decreases in mental health. The aim of My Tools 4 Care - In Care (an online intervention) is to support carers of persons living with dementia residing in LTC through transitions and increase their self-efficacy, hope, social support and mental health. This article comprises the protocol for a study to evaluate My Tools 4 Care-In Care (MT4C-In Care) by asking the following research questions: 1) Is there a 2 month (immediately post-intervention) and 4 month (2 months post-intervention) increase in mental health, general self-efficacy, social support and hope, and decrease in grief and loneliness, in carers of a person living with dementia residing in LTC using MT4C-In CARE compared to an educational control group? 2) Do carers of persons living with dementia residing in LTC perceive My Tools 4 Care- In Care helps them with the transitions they experience? METHODS: This study is a single blinded pragmatic mixed methods randomized controlled trial. Approximately 280 family carers of older persons (65 years of age and older) with dementia residing in LTC will be recruited for this study. Data will be collected at three time points: baseline, 2 month, and 4 months. Based on the feasibility study, we hypothesize that participants using MT4C-In Care will report significant increases in hope, general self-efficacy, social support and mental health quality of life, and significant decreases in grief and loneliness from baseline, as compared to an educational control group. To determine differences between groups and over time, generalized estimating equations analysis will be used. Qualitative descriptive analysis will be used to further evaluate MT4C-In Care and if it supports carers through transitions. DISCUSSION: Data collection will begin in four Canadian provinces (Alberta, Manitoba, Ontario and Saskatchewan) in February 2020 and is expected to be completed in June 2021. The results will inform policy and practice as MT4C-In Care can be revised for local contexts and posted on websites such as those hosted by the Alzheimer Society of Canada. TRIAL REGISTRATION: NCT04226872 ClinicalTrials.gov Registered 09 January 2020 Protocol Version #2 Feb 19, 2020.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.008 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 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".