Using Grounded Theory as a Method of Inquiry: Advantages and Disadvantages
Bibliographic record
Abstract
There are many challenges and criticisms attached to the conduct of research, none the least of which is a notion that much of the research undertaken in professional disciplines such as nursing may not have clinical and/or practical relevance. While there are a plethora of qualitative research methods that individuals must consider when designing research studies, one method stands out - Grounded Theory (GT). Grounded theory was developed in the early 1960’s by Glaser and Strauss. With its theoretical orientation based in sociology, GT strives to understand and explain human behavior through inductive reasoning processes (Elliott & Lazenbatt, 2005). Because of its emphasis on the utilization of a variety of data sources that are grounded in particular contexts, GT provides a natural theoretical fit when designing nursing research studies. In this article, the authors provide an overview of GT and then describe the appropriateness, advantages, and disadvantages of applying it as part of the research design process. Additionally, the authors highlight the importance of taking a reflexive position to stay engaged while interacting with the data, and explore how to apply GT theory to particular research questions and studies. Finally, the strengths and limitations of this method of inquiry as applied to nursing research using a brief case study approach is presented.
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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 | 0.297 | 0.284 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".