Caregiving Experiences with Health Information Management and Communication in Complex Home Care: Informing Technology Design for Caregivers of Older Adults
Bibliographic record
Abstract
Abstract Purpose: The objective of this study was to understand how caregivers manage and communicate health information for older adults who require complex home care, informing the design of new technologies to support patient safety in the home. Methods: The research involved semi-structured interviews with 15 caregivers, including family and hired caregivers, in Ontario, Canada. An inductive analysis was used to develop themes. Results: The findings described how participants were Updating the Caregiver Team to share health information in the home. Participants were also Learning to Improve Care & Decision-Making. However, sometimes participants experienced Conflicts within Caregiver Teams using current technologies, which may not fully meet their information management and communication needs. Conclusion: This research highlights the difficulties of caring for older adults in complex home care situations and the challenges that family and hired caregivers face when managing health information and communication. Currently, paper-based technologies are used, but there is a growing interest in digital tools that can efficiently gather and transform health information to better support decision-making. Collaborative digital systems involving family caregivers as important care team members could improve information sharing and reduce conflicts. However, implementing new technologies in this context can be difficult, and successful adoption may require systems that improve the overall caregiving experience in complex environments. This study recommends integrating caregivers as collaborators and implementing two-way communication in digital systems to enhance caregiver satisfaction. Future research should delve deeper into these complexities and prioritize designing effective tools for this crucial caregiving domain.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".