How to support care at home? Using film to surface the situated priorities of differently positioned ‘stakeholders’
Bibliographic record
Abstract
We started our book with a description of ‘traditional’ narratives for understanding the problem of dementia for our communities. We have already noted that these ‘traditional’ or common ways of thinking about policies designed to address the problem of dementia frequently frame solutions in economic terms. With growing numbers of people affected by the disease, and with limited institutional resources for meeting that demand, the home is figured within policy documents as a location best suited – that is, most economical from a policy perspective – for people living with dementia. Our first two chapters have created opportunities for us to raise questions about this common formulation. In Chapter 1 we pointed out how traditional economic formulations framing the problem of dementia might actually be excluding other, less traditional, less common, but possibly more inclusive, ways of understanding the problem of dementia. The near-exclusive framing of dementia as an economic problem means that very little space is left to describe the thickness of problems (Savaransky, 2018, p 217) associated with living, everyday, with a diagnosis of dementia. In Chapter 4 we begin to explore this thickness of problems by introducing readers to the families who shared time with us to help us know better what everyday life living with a diagnosis of dementia is like. But it is not only in everyday lives in homes where thickness can be described. In Chapter 2 we presented a ‘thick’ reading of practices associated with policies that constitute the home as the ‘best place’ for people living with dementia to receive care. We showed how, with an interest in mapping the population for the purposes of gathering knowledge of the (economic) scale of the dementia problem, programmes designed for early diagnosis are created and mobilized in the community. But we also saw how unstable those programmes are. Despite common formulations of institutions as powerful, we saw how the ideal programme requires navigation through a complex system of health and social supports. And, as even personnel associated with those health and social support programmes recognize, navigation may be experienced more as a ‘unicorn’ rather than the everyday reality of seeking help.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".