Beyond instrumental support: Mobile application use by family caregivers of persons living with dementia
Bibliographic record
Abstract
In recent years, there has been a rapid increase in technology use in dementia caregiving, particularly the use of mobile applications (apps) which are highly accessible, cost-effective and intuitive. Yet, little is known about the experiences of family caregivers of persons living with dementia who use apps to support caregiving activities. This is of particular concern given that limited understandings of the user experience in designing technology have often led to end-users experiencing barriers in technology adoption and use. Using a qualitative descriptive approach, the purpose of the study was to explore the experiences of family caregivers of persons living with dementia on using apps in their caregiving roles. A purposive sample of five family caregivers in Ontario, Canada participated in two interviews each, with the second interview informed by photo-elicitation methods. Thematic analysis of the collected data revealed a central overarching theme, Connecting to support through apps in my, your and our lives, which explicated how apps played an important role in the lives of the caregiver, the care recipient and both together as a dyad. Three core themes also emerged: Adapting apps to meet individual needs of the dyad, Minimising the impact of the condition on the person and the family and Determining the effectiveness of apps. The findings highlighted that the value of apps extends beyond their mere functionality and their ability to help with care provision as they are also able to promote richer interpersonal connections, enhance personhood and sustain family routines. This research advances our understanding of the impact of app use in caregiving and provides direction for future research, policy, education, practice and app development.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".