Posing Clinical Questions: Framing the Question for Scientific Inquiry
Bibliographic record
Abstract
Much of nursing practice is (and always has been) based on information generated through inquiry. Finding the best answers quickly and effectively for the questions that arise in the clinical setting facilitates care, increases nursing efficiency, and improves patient outcome and satisfaction. Posing clinical questions also can help nurses identify and fill in gaps in knowledge, keep up with advances in clinical practice, and strengthen interactions with their peers, team members, and patients and their families. Formulating clinical questions that lead to sound, evidence-based answers to resolve clinical problems or direct patient-care decisions takes time and practice. The information in this article will assist nurses to develop the skill of framing clinical questions efficiently and effectively.
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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 | 0.420 | 0.563 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.017 | 0.109 |
| Scholarly communication | 0.039 | 0.055 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.037 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".