Selecting a Grounded Theory Approach for Nursing Research
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".