Nurses and their ongoing engagement in nursing research: a brief commentary.
Bibliographic record
Abstract
This brief commentary is in response to the article titled: Engaging clinicians in research: Issues to consider (Dunning, 2013). The article presented an overview of nursing research that included the various definitions of nursing research, a synopsis of the paradigms associated with nursing research, and a brief presentation of how nurses can engage in research. Engaging in research is more than just producing evidence. It involves the active seeking out, critiquing, and application of research in the clinical setting. Many clinicians do not have the skills to be able to adequately read or interpret empirical evidence. I would argue that before a clinician can consider designing and implementing a study, they should have a solid understanding of the relationships between research and knowledge and theory and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.054 | 0.042 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".