Fitting Square Pegs into round Holes: Doing Qualitative Nursing Research in a Quantitative World
Bibliographic record
Abstract
The authors, as doctoral candidates and registered nurses, took on a qualitative research project investigating nursing practice in a research-intensive organization. Their aims were to explore and describe how nurses in the ambulatory care setting assist patients and families, including how nursing practice was carried out, constraints to practice, and the influence of the interprofessional milieu. Their first finding, in part because of the qualitative research design used, concerned the potential impact of the organizational ethics review process on the project. The authors discuss how the language, definition of risk, and notion of informed consent articulated in the organizational review process influenced both the research timeline and (potentially) the study itself. While not dismissing the value of ethics review, they explore the tension of overlaying generic criteria for quantitative research, specifically randomized controlled trials, on nursing research from other traditions.
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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 | 0.207 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.040 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".