Rethinking the assumptions of intervention research concerned with care at home for people with dementia
Bibliographic record
Abstract
Aging populations have been positioned as a challenge to health and social service planning around the world, a situation even more pronounced in the case of persons with a diagnosis of dementia. While policy responses emphasize that care be provided for persons with dementia in home settings for as long as possible and that family carers be supported in the provision of this care, finding good ways to support families as they do the work of ‘delaying institutionalization’ has been challenging despite decades of intervention research intended to develop and evaluate interventions to support families. In this context of limited effectiveness it is useful to examine the assumptions informing research practices. Problematization is a method of literature analysis useful for clarifying and challenging assumptions informing a field of research in order to generate new approaches to research or new research questions. Our analysis suggests that although community-based intervention research has contributed significant knowledge about the kinds of things that might help families, there are limitations related to the dominant assumptions underlying the field. We highlight three areas for re-consideration: the overriding focus on caregiver–care recipient dyads, the under-determination of the object(s) of inquiry and the algorithmic nature of interventions themselves. Issues in these areas, we argue, arise from a commitment to homogeneity characteristic of biomedical models of disease that may need to be rethought in the face of consequential heterogeneity among research populations. That is, there is a mismatch between ‘dementia’ in the intervention research literature and ‘dementia’ in the life that is consequential for families living with these concerns.
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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 | 0.616 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.011 | 0.117 |
| Scholarly communication | 0.028 | 0.050 |
| Open science | 0.015 | 0.017 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".