MétaCan
Menu
Back to cohort
Record W3042295187 · doi:10.1111/nin.12373

The necessity and possibilities of playfulness in narrative care with older adults

2020· article· en· W3042295187 on OpenAlexaff
Bodil H. Blix, Charlotte Berendonk, D. Jean Clandinin, Vera Caine

Bibliographic record

VenueNursing Inquiry · 2020
Typearticle
Languageen
FieldPsychology
TopicCounseling, Therapy, and Family Dynamics
Canadian institutionsUniversity of AlbertaWorkers Compensation Board of Alberta
Fundersnot available
KeywordsNarrativeCuriositySurpriseActive listeningPsychologyGenerative grammarAestheticsSociologyOpenness to experienceEpistemologySocial psychologyPsychotherapistLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.309
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueNursing InquirySame topicCounseling, Therapy, and Family DynamicsFrench-language works237,207