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Record W2104574437 · doi:10.1177/1049732307301235

Making a Difference in Critical Care Nursing Practice

2007· article· en· W2104574437 on OpenAlexaff
Meaghan Hawley, Louise Jensen

Bibliographic record

VenueQualitative Health Research · 2007
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsUniversity of AlbertaSt. Francis Xavier University
Fundersnot available
KeywordsExplicationLifeworldExcellenceMeaning (existential)NursingThematic analysisMeaning-makingPsychologyQualitative researchSociologyMedicineEpistemologyPsychotherapistPhilosophy

Abstract

fetched live from OpenAlex

This hermeneutic phenomenological inquiry reveals the meaning in critical care nurses' lived experiences of making a difference in practice with a view to deepening our understanding of nursing in pursuing nursing excellence. It serves to show how critical care nurses make a difference and what difference they make to critically ill patients in a manner that more fully captures nursing practice as enacted in the critical care setting. The authors subjected transcripts of the conversations with 16 critical care nurse participants to a thematic analysis and reflective process from which the following themes emerged: making the inhumane humane, making the unbearable bearable, making the life threatening life sustaining, and making the unlivable livable. Guided by the lifeworld existentials, these themes became the threads around which an interpretive-descriptive text was written. The authors wove other relevant sources of lived-experience material into the evolving text to assist with the explication of meaning.

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)0.017
metaresearch head score (Gemma)0.037
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 score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0180.065
Scholarly communication0.0170.016
Open science0.0020.011
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.870
GPT teacher head0.782
Teacher spread0.089 · 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

Citations29
Published2007
Admission routes1
Has abstractyes

Explore more

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