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Record W4244751068 · doi:10.32920/ryerson.14653662.v1

Nurses' experiences of creating an artistic instrument for their nursing practice and professional development: an arts-informed narrative inquiry

2021· preprint· en· W4244751068 on OpenAlexaff
Neelam Walji

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNarrativeNarrative inquiryThe artsEmpathyMeaning (existential)Transformative learningPsychologyReflective practiceTemporalityNursingSociologyPedagogyVisual artsMedicineArtSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

My passion for the arts as a medium motivated me to create an art piece (artistic instrument) to enrich my nursing practice. This inspired me to explore how other nurses experience creating their own artistic instruments and what meaning these held for their nursing practice and professional development. In this arts-informed Narrative Inquiry, two participants engaged in a narrative interview and in the Narrative Reflective Process, an artistic approach to creative reflection. Participants’ stories were re-constructed and analyzed using the Narrative Inquiry three-dimensional space (temporality, sociality, and place), and examined through the theoretical lens of Patterns of Knowing. Findings revealed six narrative threads (empathy, quality of life, communication, power imbalances, and personal as well as professional development) highlighting the importance of person-centered care, the value of reflective practice, and the need for further research exploring the use of arts by healthcare providers across diverse educational and practice based settings.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.025
Scholarly communication0.0130.007
Open science0.0020.015
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.089
GPT teacher head0.441
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2021
Admission routes1
Has abstractyes

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