Doing Phenomenological Research Collaboratively
Bibliographic record
Abstract
Collaborative research between nurses employed in the academic and practice sectors is a cost-effective and innovative way to investigate aspects of clinical practice, articulate clinical and teaching expertise, and extend professional practice knowledge. In collaborative ventures, researchers from different institutional cultures often work together to investigate a particular area of interest. This poses challenges in relation to the perceptions, understandings, and interpretations of the research question and of the mode of inquiry, particularly when investigating through the qualitative paradigm. The purpose of this article is twofold. The first is to clarify some of the challenges experienced while conducting collaborative research and describe the steps taken to ensure consistency between the purpose of the research and the phenomenological research design used to explore the learning that nursing students acquire in their final clinical practicum. Second, it was thought that by illuminating this learning, registered nurses working as preceptors and those supporting new graduates could gain insight into the complexities of learning the skills of safe and competent practice from the student's perspective. This insight is essential in creating a strategy between education and practice to minimize the duplication of learning opportunities and lessen the cost of supporting newly registered nurses, which may be at the expense of investment in the professional development of experienced registered nurses.
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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 | 0.102 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".