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Record W2808293213 · doi:10.1097/njh.0000000000000478

Can Writing and Storytelling Foster Self-care?

2018· article· en· W2808293213 on OpenAlexaff
Anne Bruce, Helena Daudt, Susan Mary Fownes Breiddal

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2018
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsCanadian Hospice Palliative Care AssociationUniversity of Victoria
Fundersnot available
KeywordsStorytellingFacilitatorNarrativePsychologyNursingHealth careThrivingMeaning (existential)MedicineSocial psychologyPsychotherapistArt

Abstract

fetched live from OpenAlex

Research into self-care practices suggests the need for conscientious and systematic support of nurses and other health care providers. The purpose of this study was to explore the impact of an innovative self-care initiative. The goals were to explore the experience of nurses and other health care providers participating in a reflective, creative nonfiction storytelling event called "Dinner and Stories" and the potential benefits and limitations of using an informal, storytelling model for self-care. A qualitative narrative design was used. Twenty-seven participants including nurses, social workers, and hospice volunteers wrote creative nonfiction stories about a lingering experience of providing care. At predefined dates, groups of up to six met for dinner in a home setting. Participants read aloud, listened deeply, and discussed their narrated stories. Four sources of data were collected: creative nonfiction stories, online forum discussions, in-depth interviews, and host facilitator field notes. Researchers identified four themes: (1) needing a self-care culture, (2) storytelling and writing as healing, (3) co-creating layers of connection, and (4) preferring face-to-face contact. Results add to knowledge about the therapeutic benefits of writing and storytelling for nurses and other health care providers including enriched meaning-making, emotional conveyance, and therapeutic connections between storytellers and listeners.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score0.235

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 head0.024
GPT teacher head0.337
Teacher spread0.313 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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