Honoring the spirit of research within: The yoga and more (CAM) research interest group
Bibliographic record
Abstract
This article explores the means and methods by which nurses can become actively involved in the search for answers through the research process. It presents one unique example that illustrates research involvement for nurses and other healthcare providers at all levels of practice with a focus on complementary and alternative medicine (CAM). The establishment of the Yoga and More (CAM) Research Interest Group (RIG) at a research-intensive college of nursing, and the members’ involvement in a funded pilot study on yoga for lung cancer survivors is presented. The starting point included establishing the research questions and potential solutions, and reinforces the belief that research promotes improved care, better patient outcomes, enhanced quality of life, and increased satisfaction among care providers. In addition, we provide an example of how active involvement in the research process with supportive mentorship promotes “learning-in-action” and stimulates continued interest and growth in the research process. The development and evolution of this innovative research initiative is discussed from a theoretical, methodological and personal perspective with implications for nurses seeking to become involved in the research process.
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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 | 0.083 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".