TOO MUCH INFORMATION? EXPLORING THE ROLE OF ‘INFORMATION BURDEN’ IN THE EXPERIENCES OF CAREGIVERS FOR OLDER ADULTS
Bibliographic record
Abstract
With the onus of care for the elderly shifting onto informal caregivers, older adults may often be accompanied by family caregivers to medical visits. In order to optimize care and to participate in healthcare decision-making for the older patient, caregivers are often privy to significant health information. This may include personal details about the patient previously unknown to the caregiver, information about advance care and end-of-life planning, and a ‘bad news’ diagnosis. However, a commonly overlooked issue in geriatric practice is the amount of information that caregivers must acquire and manage as patients get older and more dependent on them. Caregivers, often deemed the ‘invisible second patient’, may feel inundated with the health information shared and feel unprepared, vulnerable and in need of healthcare support themselves. Using a qualitative research design, 49 in-depth interviews with 23 caregivers of older adults were conducted over a year. Our results illustrate how caregivers often provide care and conceal information from loved ones at the detriment to their own health and social well-being. Interview data highlights the complexity of managing health information with some gender differences in caregiving experiences. Results suggest that caregivers sometimes felt emotionally compromised when making objective healthcare decisions, and that ‘too much information’ resulted in information burden, conflict and a desire to override patient wishes out of compassion and fear. These findings may impact care by illustrating that caregiver involvement should trigger further dialogue between doctors, elderly patients and their caregivers, especially with respect to information sharing boundaries.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".