Capturing Home Care Information Management and Communication Processes Among Caregivers of Older Adults: Qualitative Study to Inform Technology Design
Bibliographic record
Abstract
BACKGROUND: The demand for complex home care is increasing with the growing aging population and the ongoing COVID-19 pandemic. Family and hired caregivers play a critical role in providing care for individuals with complex home care needs. However, there are significant gaps in research informing the design of complex home care technologies that consider the experiences of family and hired caregivers collectively. OBJECTIVE: The objective of this study was to explore the health documentation and communication experiences of family and hired caregivers to inform the design and adoption of new technologies for complex home care. METHODS: The research involved semistructured interviews with 15 caregivers, including family and hired caregivers, each of whom was caring for an older adult with complex medical needs in their home in Ontario, Canada. Due to COVID-19-related protection measures, the interviews were conducted via Teams (Microsoft Corp). The interview guide was informed by the cognitive work analysis framework, and the interview was conducted using storytelling principles of narrative medicine to enhance knowledge. Inductive thematic analysis was used to code the data and develop themes. RESULTS: Three main themes were developed. The first theme described how participants were continually updating the caregiver team, which captured how health information, including their communication motivations and intentions, was shared among family and hired caregiver participants. The subthemes included binder-based health documentation, digital health documentation, and communication practices beyond the binder. The second theme described how participants were learning to improve care and decision-making, which captured how they acted on information from various sources to provide care. The subthemes included developing expertise as a family caregiver and tailoring expertise as a hired caregiver. The third theme described how participants experienced conflicts within caregiver teams, which captured the different struggles arising from, and the causes of, breakdowns in communication and coordination between family and hired caregiver participants. The subthemes included 2-way communication and trusting the caregiver team. CONCLUSIONS: This study highlights the health information communication and coordination challenges and experiences that family and hired caregivers face in complex home care settings for older adults. Given the challenges of this work domain, there is an opportunity for appropriate digital technology design to improve complex home care. When designing complex home care technologies, it will be critical to include the overlapping and disparate perspectives of family and hired caregivers collectively providing home care for older adults with complex needs to support all caregivers in their vital roles.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 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".