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Record W3199853268 · doi:10.1111/nin.12462

Narrative care: Unpacking pandemic paradoxes

2021· article· en· W3199853268 on OpenAlexaff
Vera Caine, Pamela Steeves, Charlotte Berendonk, Bodil H. Blix, D. Jean Clandinin

Bibliographic record

VenueNursing Inquiry · 2021
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsAlberta Advanced EducationUniversity of Alberta
Fundersnot available
KeywordsNarrativeLiminalityEmbodied cognitionNarrative inquirySociologyHealth careAestheticsPsychologyEpistemologyPolitical scienceLiteratureArt

Abstract

fetched live from OpenAlex

During the coronavirus disease 2019 pandemic, public health has issued three interrelated dominant narratives through social media and news outlets: First, to care for others, we must keep physically distant; second, we live in the same world and experience the same pandemic; and third, we will return to normal at some point. These narratives create complexities as they collide with the authors' everyday lives as nurses, educators, and women. This collision creates three paradoxes for us: (a) learning to care by creating physical distance, (b) a sense of togetherness erases inequities, and (c) returning to normal is possible. To inquire into these three paradoxes, we draw on our experiences with Ingrid, an older adult who requires in-home physical care, and Matthew, a man with multiple disabilities including severe oral dyspraxia and developmental delays. We outline how narrative care is a counterstory to the dominant narratives and enables us to find ways to live our lives within the paradoxes. Narrative care allows us, through attention to embodiment, liminality, and imagination, to create forward looking stories. Understanding narrative care within these paradoxes allows us to offer more complex understandings of the ways narrative care can be embodied in our, and others', lives.

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 score0.095
Threshold uncertainty score0.329

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 studies0.0000.000
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 head0.063
GPT teacher head0.387
Teacher spread0.324 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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