The necessity and possibilities of playfulness in narrative care with older adults
Bibliographic record
Abstract
For us, narrative care is grounded in pragmatist philosophy and focused on experience. Narrative care is not merely about acknowledging or listening to people's experiences, but draws attention to practical consequences. We conceptualize care itself as an intrinsically narrative endeavour. In this article, we build on Lugones' understanding of playfulness, particularly to her call to remain attentive to a sense of uncertainty, and an openness to surprise. Playfulness cultivates a generative sense of curiosity that relies on a close attentiveness not only to the other, but to who we each are within relational spaces. Generative curiosity is only possible if we remain playful as we engage and think with experiences and if we remain responsive to the other. Through playfulness, we resist dominant narratives and hold open relational spaces that create opportunities of retelling and reliving our experiences. Drawing on our work alongside older adults, as well as people who work in long-term care, we show the possibilities of playfulness in the co-composition of stories across time. By intentionally integrating playfulness, narrative care can be seen as an intervention, as well as a human activity, across diverse social contexts, places and times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".