Building qualitative study design using nursing's disciplinary epistemology
Bibliographic record
Abstract
AIM: To discuss the implications of drawing on core nursing knowledge as theoretical scaffolding for qualitative nursing enquiry. BACKGROUND: Although nurse scholars have been using qualitative methods for decades, much of their methodological direction derives from conventional approaches developed for answering questions in the social sciences. The quality of available knowledge to inform practice can be enhanced through the selection of study design options informed by an appreciation for the nature of nursing knowledge. DESIGN: Discussion paper. DATA SOURCES: Drawing on the body of extant literature dealing with nursing's theoretical and qualitative research traditions, we consider contextual factors that have shaped the application of qualitative research approaches in nursing, including prior attempts to align method with the structure and form of disciplinary knowledge. On this basis, we critically reflect on design considerations that would follow logically from core features associated with a nursing epistemology. IMPLICATIONS FOR NURSING: The substantive knowledge used by nurses to inform their practice includes both aspects developed at the level of the general and also that which pertains to application in the unique context of the particular. It must be contextually relevant to a fluid and dynamic healthcare environment and adaptable to distinctive patient conditions. Finally, it must align with nursing's moral mandate and action imperative. CONCLUSION: Qualitative research design components informed by nursing's disciplinary epistemology will help ensure a logical line of reasoning in our enquiries that remains true to the nature and structure of practice knowledge.
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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 | 0.243 | 0.206 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".