BIG OR SMALL STORIES? TWO WAYS TO APPROACH NARRATIVE CARE IN CANADIAN LONG-TERM CARE SETTINGS
Bibliographic record
Abstract
Presenting subjective experiences through stories is of great importance to the identities of human beings. Therefore, persons can be considered to be ‘narrative beings’. However, residents living in nursing homes often experience barriers expressing their stories – partly due to cognitive or physical impairments, partly due to a lack of persons stimulating their stories or listening to them. This threatens residents’ narrative identities. Care providers can use approaches, such as life story work or narrative care to support residents in remembering and continuing to compose their stories. These approaches honor residents’ life stories and foster identities. We were interested in how nursing homes in Canada have taken up the practice of narrative care. In a qualitative study we engaged in conversations with ten experts in narrative care and five long-term care providers. We analyzed the data using content analysis and identified two key approaches to foster residents’ narrative identity development in practice. One approach, which we refer to as the ‘big story’ approach, focused on gaining whole life stories and producing life story books and DVDs about residents’ lives. Other approaches reflected ‘small stories’; here narrative care is seen as the co-composition of identities that grows through the interactions of care providers and residents, fostering the ongoing and ordinary communications necessary for identity development. We will highlight characteristics, possibilities and challenges of big and small story approaches, particularly in relation to the implementation and sustainability of narrative care in nursing home settings.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.035 | 0.037 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".