Learning to navigate and enriching the research process by engaging in a collaborative practice research capacity strategy
Bibliographic record
Abstract
Engaging regulated health professionals in research is associated with greater service efficiencies and positive patient outcomes (reduced patient mortality and morbidity). This paper provides the results from a study undertaken to explore the perspectives and experiences of nurses and health disciplines participating in a collaborative practice based research (CPBR) capacity building educational program. The purpose of this study was to explore the perceptions and experiences of nurses and other health disciplines in an interprofessional, collaborative research capacity building strategy. The interviews were analyzed using an inductive, thematic analysis process. Twelve members participating in the CPBR program who were female with 5 nurses, 3 occupational therapists, 2 social workers, 1 speech language pathologist and 1 research coordinator were recruited for the study. The following five themes emerged from the data: 1) learning to navigate the research landscape in a shared space; 2) providing an opportunity and support for interprofessional clinician driven research; 3) enriching the research process by engaging different professions to collaborate; 4) impacting current and future collaborative practice; and 5) keeping the momentum amidst experiencing challenges. Our study demonstrated the value of providing opportunity for nurses and health disciplines to engage in collaborative practice based research and undertake a project relevant to clinical practice that adds to the body of knowledge on the value of collaborative practice based research capacity building strategies and communities of practice.
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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 | 0.191 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.025 | 0.018 |
| Open science | 0.006 | 0.040 |
| Research integrity | 0.007 | 0.009 |
| 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".