The Hope Research Community of Practice
Bibliographic record
Abstract
Background Clinical nurses have multiple challenges in conducting high-quality nursing research to inform practice. Theoretically, the development of a community of practice on nursing research centered on the concept of hope is an approach that may address some of the challenges. Purpose This article describes the delivery and evaluation of a hope research community of practice (HRCoP) approach to facilitate research expertise in a group of advanced practice nurses in one hospital. It addressed the question: Does the establishment of a HRCoP for master's prepared nurses increase their confidence and competence in leading nursing research? Method Using interpretive descriptive qualitative research methodology, five participants were interviewed about their experiences within the HRCoP and facilitators engaged in participant observation. Results At 13 months, only four of the original seven participants remained in the HRCoP. While all participants discussed positive impacts of participation, they identified challenges of having protected time to complete their individual research projects, despite having administrative support to do so. Progress on individual research projects varied. Conclusion Nurse-led research remains a challenge for practicing nurses despite participating in an evidence-based HRCoP. Lessons learned from this project can be useful to other academic clinical partnerships.
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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 | 0.154 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".