Providing Tech Support as Care Work Among Care Workers in Assisted Living Facilities: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Technology can enhance the quality of life and social engagement for older adults; yet, many require assistance to use it effectively. In assisted living facilities, care workers play a crucial role in supporting residents' use of technology. However, little research has examined the experiences and challenges of care workers in this context. OBJECTIVE: In this study, we examine the perspectives and experiences of care workers in supporting older residents with technology. METHODS: We conducted semistructured interviews with 20 professional care workers in the United States, with an average of 4.9 years of experience working in assisted living facilities. RESULTS: The findings indicate that participants regularly provided technology support to their residents, while their perspectives on and acceptance of this technology support role varied. For many, this responsibility placed additional demands on their existing workload, requiring both explicit and nuanced physical and emotional labor input. CONCLUSIONS: These findings underscore the need for greater recognition and systemic support for care workers as they increasingly take on the responsibility of facilitating technology use in residential care settings.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".