Working with resistance, tension and objectivity: Conducting a randomised controlled trial of a nursing intervention for breathlessness
Bibliographic record
Abstract
It is vital that nurses wishing to recommend or introduce new strategies are able to provide supporting evidence that is acceptable to their colleagues. The methodology from which to derive such evidence remains to be clearly defined, as the research process is complex, demanding and, to a certain extent, uncharted. This paper examines the experience of nurses collaborating in a multi-centre randomised controlled trial which evaluated a nursing intervention for the management of breathlessness in patients with lung cancer. The study raised several important methodological issues: resistance among colleagues to innovative nursing practice; the difficulty of measuring well-being in patients whose physical condition is deteriorating; maintaining uniformity of practice within a diverse group of collaborating nurse researchers; and the tension between the nursing role and the necessity of an ethically demanding research design. Analysis of the process of conducting a randomised controlled trial produced valuable insights which indicated the kind of support required to undertake research and successfully implement a new intervention into clinical practice. The study also highlighted the problems associated with asking ill people to complete standard measurement tools, particularly when such instruments might not be sensitive to the reality of the patient(s) problem, in this case, the experience of breathlessness.
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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 | 0.366 | 0.437 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".