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Record W3017339795 · doi:10.1097/hnp.0000000000000379

Narrative-Based Practice

2020· article· en· W3017339795 on OpenAlexaff
Francesco Burrai, Mariangela Mettifogo, Valentina Micheluzzi, Flavia Emanuela Ferreira, Leonardo Pinna, Emma Magavern

Bibliographic record

VenueHolistic Nursing Practice · 2020
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMichel-Sarrazin
Fundersnot available
KeywordsNarrativePsychosocialFeelingNarrative inquiryNarrative medicineIsolation (microbiology)PsychologyHealth careQualitative researchIdentity (music)Quality of life (healthcare)MedicineNursingPsychotherapistSociologySocial psychologyAesthetics

Abstract

fetched live from OpenAlex

Narrative-based practice has been developed to bring the health care aspects of illness and treatment closer to the psychosocial and life experiences of a patient. It gives value to the lived experience by using writing tools, spoken words, poetry, drawing, and photography. Nephrology has become one of the first health care fields, likely due to its large patient burden of both critical and chronic disease, to use narrative-based practice. The use of narrative-based practice in renal care explores the lived experience through structured and semistructured interviews with patients, caregivers, and health care providers. The principle topics discussed are the lack of a "disease identity" that would allow patients to identify themselves with a specific state of illness, the "uncertainty" of living with an illness characterized by continuous progression and regression, and the living with the "unspeakable" looming specter of death. This review highlights the powerful significance of qualitative knowledge gained with the narrative method. Increased awareness of these aspects of patients' lived experiences can help nurses improve the quality and effectiveness of the therapeutic relationship between patient and health care professional and may offer a promising approach, within this relationship, to decreasing patient feelings of isolation.

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)0.055
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.055
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 integrity0.0000.001
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 head0.081
GPT teacher head0.428
Teacher spread0.346 · 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.

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

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