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Record W3217731002 · doi:10.32920/ryerson.14638866.v1

A thematic analysis of nursing students’ end-of-life knowledge

2021· preprint· en· W3217731002 on OpenAlexaboutno aff
Edwards Susanna

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumThematic analysisNursingTheme (computing)Nurse educationEnd-of-life careMedical educationMedicineDutyPsychologyPedagogyQualitative researchPalliative careSociology

Abstract

fetched live from OpenAlex

Background: Nursing Associations recommend that undergraduate nursing programs integrate end-of-life content throughout their nursing curricula to address the needs of those requiring such care. Objective: To determine the extent of nursing students' knowledge about end-of-life care in the final year of an undergraduate nursing program in Ontario, Canada. Method: The content of reflective exercises written by 24 nursing students enrolled in an end-of-life elective were thematically analyzed, both at the beginning and end of a 12 week course. Results: Results indicate that undergraduate nursing students’ end-of-life knowledge and experiences vary greatly. The overarching theme Duty of Care indicated that the students were motivated to take the course to fulfill a sense of professional responsibility. The sub-themes Assumptions and Experience (subdivided as Limited, Personal, and Professional) depicted students' initial and variable understanding of end-of-life care. Under the theme of Transferable Skills, the students’ range of knowledge and the competencies they gained from their clinical placements and the course were illustrated. Conclusion: The study is expected to aid in curriculum review of a university's undergraduate nursing program.

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)0.015
metaresearch head score (Gemma)0.017
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 score0.015
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.008
Science and technology studies0.0030.004
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.238
GPT teacher head0.503
Teacher spread0.266 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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