DigiTRAC: Qualitative insights from knowledge users to inform the development of a Digital Toolkit for enhancing resilience among multiple sclerosis caregivers
Bibliographic record
Abstract
BACKGROUND: Resilience-promoting resources are critically needed to support positive caregiving experiences for multiple sclerosis (MS) caregivers. A digital toolkit offers a flexible way to access and use evidence-based resources that align with MS caregivers' interests and needs over time. OBJECTIVE: We explored the perspectives of key knowledge users regarding content areas, features, and other considerations to inform an MS caregiver resilience digital toolkit. METHODS: Twenty-two individuals completed a demographic survey as part of this study: 11 MS family caregivers, 7 representatives of organizations providing support services for people with MS and/or caregivers, and 4 clinicians. We conducted nine semi-structured individual interviews and two focus groups. Data were analyzed using content analysis. RESULTS: Participants recommended that a digital toolkit should include content focused on promoting MS caregivers' understanding of the disease, its trajectory and available management options, and enhancing caregiving skills and caregivers' ability to initiate and maintain behaviours to promote their own well-being. Features that allow for tracking and documenting care recipients' and caregivers' experiences, customization of engagement, and connectivity with other sources of support were also recommended. Participants suggested a digital toolkit should be delivered through an app with web browser capabilities accessible on smartphones, tablets, or laptops. They also acknowledged the need to consider how users' previous technology experiences and issues related to accessibility, usability, privacy and security could influence toolkit usage. CONCLUSION: These findings will guide future toolkit development and evaluation. More broadly, this study joins the chorus of voices calling for critical attention to the well-being of MS family caregivers.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".