Using a Flexible Diary Method Rigorously and Sensitively With Family Carers
Bibliographic record
Abstract
Health and social science researchers are increasingly interested in the range of new possibilities and benefits associated with diary methods, particularly using digital devices. In this article, we explore how a flexible diary method, which enables participants to choose the device (i.e., paper notebook, tablet, or computer) and medium (i.e., text, photographs, sketches) through which they narrate their experiences, can be used to promote sensitive and rigorous research engagement with family carers to people with dementia. We used a diary interview method with 10 carers over the course of 6 weeks to explore how they experience and interpret the changing behaviors of their cognitively impaired kin. We reflect on how the quality of diary data can be enhanced alongside the ethical dimensions of research with carer populations, through different forms of diary keeping, regular interaction with participants, reflexive practice, and follow-up interviews.
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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 | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".