What can qualitative studies offer in a world where evidence drives decisions?
Bibliographic record
Abstract
In an environment in which evidence-based practice is the espoused norm, nurses have understandably sought to frame the knowledge they deem relevant to practice decisions, including the findings of their qualitative studies, as a form of evidence. However, since cancer patients face a significant challenge interpreting various evidence claims, it is important to recognize that the results of our qualitative studies reflect a different form of knowledge from that which an evidence-based practice definition of evidence presumes. Thus, we need to rethink our relationship to what qualitative studies offer to the evidentiary dialog. An approach to qualitative inquiry that derives from a nursing disciplinary logic model is, therefore, presented as an alternative means by which to generate the kinds of knowledge nurses need to practice and to gain expertise in clinical wisdom. Drawing on cancer communications research as an example, a nursing angle of vision on how best to use qualitative approaches to interpret evidence and inform practice emerges.
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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 | 0.600 | 0.671 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.025 | 0.119 |
| Scholarly communication | 0.077 | 0.142 |
| Open science | 0.014 | 0.032 |
| Research integrity | 0.043 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".