Interviewing Family Caregivers: Implications of the Caregiving Context for the Research Interview
Bibliographic record
Abstract
Family caregiving tends to involve strong and often competing emotional experiences. Most of our knowledge of caregiving stems from interview research, much of it cross-sectional in nature. In this article we explore the implications of interviews as a research method for understanding caregiving. Specifically, we address difficulties in interpreting participants' talk about caregiving when this talk is simultaneously an articulation of experience and an attempt to cope with that experience. Either uncritically accepting accounts as reflective of experience, without considering the role of coping, or making assumptions about the success of caregiver coping in this context, might be erroneous. Our own experiences of interviewing family caregivers in different research projects will be drawn upon as examples. We conclude by questioning the ability to draw conclusions about caregiving and/or caregiver coping based solely on interview research, and call for greater integration of observational and longitudinal methods in family caregiving research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".