Person-centredness in graduate nursing education: practice development in action
Bibliographic record
Abstract
Background: Person-centredness is an approach that views each individual as a unique being, and is supported by the values of mutual respect and individual right to self-determination. This approach is currently a prevailing principle in policy, education and practice settings globally. The relevance of person-centredness to postgraduate student learning is immense, as new graduate nurses are expected to assume leadership and advocacy roles in healthcare environments and engage with key stakeholders in the exchange of knowledge to inform practice. The application of person-centred practices by faculty within postgraduate nursing programmes is therefore instrumental in providing students with the necessary supportive environments to acquire the skills for person-centred care. \nMethod: Practice development was used as a foundation for implementing innovative teaching methods for postgraduate nursing students. Nurses enrolled in the advancement of professional nursing practice seminars and practicum, participating in various active learning and critical reflection activities throughout the semester. \nImplications for practice: \nPractice development provides an innovative foundation for postgraduate nursing education \nEducators should consider this unique and person-centred approach as an alternative to the typical pedagogical approach
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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 | 0.055 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".