Metaphor as methodology: Methodological reflections on visualizing the dementia journey
Bibliographic record
Abstract
Metaphors to describe and understand dementia have been used in Western culture for many years. However, the ways in which people living with dementia and care partners use metaphors and symbols to illustrate and give meaning to their own experiences has been less understood. In this paper we explore the use of metaphor as methodology-- a way to support people living with dementia and their care partners in reflecting on and sharing their experiences of dementia. More specifically, drawing on our experiences using metaphor and symbols to map out the dementia journey from the perspectives of people living with dementia, care partners, and health and social care providers in Ontario, Canada, we describe our process of employing metaphor as methodology. We reflect on the use of metaphor as methodology through framing the dementia experience, exploring complexity, and representing multidimensionality. The use of metaphors has the potential to open space for new understandings of dementia.
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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 | 0.053 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.023 | 0.080 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".