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Record W4288062326 · doi:10.26634/jnur.11.4.18364

Grounded theory approach in nursing practice

2022· article· en· W4288062326 on OpenAlexaff
Saleem Neelam

Bibliographic record

Venuei-manager’s Journal on Nursing · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGrounded theoryEpistemologyRelativismOntologyConstructivist grounded theoryQualitative researchPerspective (graphical)Social constructivismNursing theoryContext (archaeology)SociologyPsychologyComputer scienceSocial sciencePhilosophyMEDLINEArtificial intelligence

Abstract

fetched live from OpenAlex

Grounded theory is a methodological approach that aims to generate a theory that is grounded in systematically gathered data and its analysis through an inductive process. Annells (1997) defined grounded theory methodology as a qualitative approach to an inquiry that is embedded in relativist ontology and subjectivist epistemology. The purpose of grounded theory is to generate a theory that is rooted in the participant's perspective involved in the study. This paper aims to explicate the ontological and epistemological points of view of the constructivist paradigm. The constructivist paradigm has a relativist ontology and a subjective epistemology (Guba & Lincoln, 1994) . A critique of grounded theory methodology is discussed inconsideration of diverse factors such as health and power-related inequalities, environment, social context, culture, gender, and social status. In nursing research, grounded theory is increasingly used by researchers. This paper will highlight the importance of grounded theory research for nursing practice and the generation of theories that are rooted in real clinical practices (Lazenbatt & Elliott, 2005) .

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)0.079
metaresearch head score (Gemma)0.068
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.068
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0080.010
Science and technology studies0.0040.018
Scholarly communication0.0120.007
Open science0.0060.010
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0100.003

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 head0.115
GPT teacher head0.540
Teacher spread0.425 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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