Bibliographic record
Abstract
BACKGROUND: Grounded theory methodology is a suitable qualitative research approach for clinical inquiry into nursing practice, leading to theory development in nursing. Given the variations in, and subjectivity attached to, the manner in which qualitative research is carried out, it is important for researchers to explain the process of how a theory about a nursing phenomenon was generated. Similarly, when grounded theory research reports are reviewed for clinical use, nurses need to look for researchers' explanations of their inquiry process. AIM: The focus of this article is to discuss the practical application of grounded theory procedures as they relate to rigour. METHOD: Reflecting on examples from a grounded theory research study, we suggest eight methods of research practice to delineate further Beck's schema for ensuring, credibility, auditability and fittingness, which are all components of rigour. FINDINGS: The eight methods of research practice used to enhance rigour in the course of conducting a grounded theory research study were: (1) let participants guide the inquiry process; (2) check the theoretical construction generated against participants' meanings of the phenomenon; (3) use participants' actual words in the theory; (4) articulate the researcher's personal views and insights about the phenomenon explored; (5) specify the criteria built into the researcher's thinking; (6) specify how and why participants in the study were selected; (7) delineate the scope of the research; and (8) describe how the literature relates to each category which emerged in the theory. CONCLUSIONS: The eight methods of research practice should be of use to those in nursing research, management, practice and education in enhancing rigour during the research process and for critiquing published grounded theory research reports.
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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 | 0.286 | 0.423 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".