A recipe for mealtime resilience for families living with dementia
Bibliographic record
Abstract
To date, research delving into the narratives of persons living with dementia is limited. Taking part in usual mealtime activities such as preparing food can sustain the identity of persons living with dementia. Yet if capacity for mealtime activities changes, this can put a strain or demand on the family, which must adjust and adapt to these changes. The aim of this study was to develop an in-depth story of resilience in one family living with dementia that was experiencing mealtime changes. Thematic narrative analysis following the elements of Clandinin and Connelly's (2000) 3D narrative inquiry space was used. One family's dementia journey was highlighted using the metaphor of a baking recipe to reflect their story of resilience. Developing positive strategies and continuing to learn and adapt were the two approaches used by this resilient family. Reminiscing, incorporating humour, having hope and optimism, and establishing social support were specific strategies. This family continued to learn and adapt by focusing on their positive gains and personal growth, accumulating life experiences, and balancing past pleasures while adapting to the new normal. Future work needs to further conceptualise resilience and how it can be supported in families living with 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".