Evidence-based practice: implications for the practising oncology nurse.
Bibliographic record
Abstract
While the premises of EBP seem highly appropriate on the surface, disquieting questions are raised when one examines the implications for the practising oncology nurse of attempting to implement EBP. There are clinical situations for which no empirical evidence exists on which to base nursing decisions. In some cases, empirical evidence is sparse and based on a mix of small, descriptive studies. Although guidelines exist for examining purely quantitative literature and are being developed for examining purely qualitative literature, no guidelines exist for evaluating a mixture. The process of operationalizing EBP is time-consuming and requires expertise which many oncology nurses lack. Does this mean that EBP is a laudable but impossible enterprise? I don't think so. Nurses will need to incorporate the empirical literature as well as other sources of knowledge to inform their clinical decision-making. In addition, oncology nurses need to apply and adapt models, such as Howell and Pelton's (2001), to make the best use of all sources of knowledge to facilitate excellent patient care.
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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 | 0.127 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".