Bibliographic record
Abstract
ABSTRACT In this paper we use Alvesson and Sandberg's strategy of problematisation to analyse the assumptions embedded in the development and use of the concept of ‘care-giver burden’. We do this in order to develop an explanation as to why decades of research into the experience of providing home-based care to a family member with dementia has had little effect in relieving or reducing the ‘burden’ of that care. Though some part of this is undoubtedly political, our analysis suggests that key assumptions of the research limit both knowledge development and intervention effectiveness. Especially problematic are first, an overriding focus on the isolated care-giver–recipient dyad as the appropriate object of inquiry and target of intervention, and second, an absence of an analysis of the materiality of care and care-giving practices. The heterogeneity of care situations, including interrelations among people, technologies, objects, spaces and other organisational worlds, appear in much of the research primarily as methodological problems, variables to be subdued through a more rigorous application of method. The high volume of research and acknowledged low impact of interventions, however, suggests that rethinking the nature of care practices, and how we come to know about them, is necessary if we are to develop and implement strategies that will contribute to better outcomes for people.
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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 | 0.227 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.016 | 0.028 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".