Reflections in Education: The value of a post-graduate clinical placement for nursing students
Bibliographic record
Abstract
The importance of a clinical placement in graduate nursing programs has been recognized in the academic literature and in the advanced nursing practice workforce environment as being beneficial. During a placement, post-graduate students have opportunities to familiarize themselves with the Advanced Practice Nurse (APN) role and competencies, build networks, advance their personal and professional development, and validate their motivation and desire to pursue a particular professional area of advanced nursing practice. This paper describes reflections on a post-graduate clinical placement in palliative care, exercising the Canadian Nursing Association’s (2019) APN competencies. Exposure to such a placement helps novice APNs develop a strategic and grounded advanced nursing practice foundation.
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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 | 0.016 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".