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Record W2895681440 · doi:10.1177/2333393618799571

Selecting a Grounded Theory Approach for Nursing Research

2018· article· en· W2895681440 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2018
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsGrounded theoryField (mathematics)EpistemologyComputer scienceKey (lock)Qualitative researchManagement scienceSociologySocial scienceMathematics

Abstract

fetched live from OpenAlex

Grounded theory is a commonly used research methodology. There are three primary approaches to grounded theory in nursing research: those espoused by Glaser, Strauss and Corbin, and Charmaz. All three approaches use similar procedures, yet there are important differences among them, which implies that researchers need to make careful choices when using grounded theory. Researchers new to grounded theory need to find the most appropriate approach that fits their research field, topic, and researcher position. In this article, we compare the three grounded theory approaches. Choices of a grounded theory approach will depend on the researcher's understanding of the philosophical underpinnings of all three approaches. Practical aspects of grounded theory approaches should match the information processing styles and analytical abilities of the researcher and the intended use of the theory. We illustrate key aspects of decision making about which method to select by drawing upon the first author's experiences in his doctoral research.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.567
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0040.005
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.276
GPT teacher head0.608
Teacher spread0.332 · 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