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Record W4281388976 · doi:10.32920/19749958

The reciprocal relationship of self and practitioner: a narrative inquiry self-study

2022· preprint· en· W4281388976 on OpenAlexaff
Christina Di Stasi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsNarrativeExperiential learningPsychologyProfessional developmentNarrative inquiryVulnerability (computing)PedagogyReflective practiceMedical educationSociologyMedicine

Abstract

fetched live from OpenAlex

In an increasingly demanding environment like healthcare, nurses may often be meeting their professional responsibilities at the expense of their personal needs. Existing research shows the benefits of engaging in experiential learning, such as reflective practices. By using reflective practices, nurses can reveal challenges in the workplace that affect their professional performance, opening discussion for how these could be mitigated. By engaging in a self-study, using Connelly and Clandinin’s Narrative Inquiry, I explore the mutually informing nature of personal and professional formation. I tell personal stories of my childhood and professional stories of my time as a nursing student, new graduate and current practitioner. Using the analytical framework of Narrative Inquiry, I identify two narrative patterns (vulnerability and belonging) in my lived and told stories. I highlight how social interactions influence our personal and professional identities, and how this understanding offers development opportunities to benefit the nurse-client relationship and society.

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)0.018
metaresearch head score (Gemma)0.028
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 score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0130.023
Scholarly communication0.0140.011
Open science0.0020.011
Research integrity0.0030.005
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 head0.058
GPT teacher head0.375
Teacher spread0.317 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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