Bibliographic record
Abstract
Background: This article describes, from a reflective stance, my experiences of exploring the concept of person-centred culture (McCormack and McCance, 2017) in healthcare, as an undergraduate nursing student. It also examines my early attempts to apply person-centred practices. I will share how I began to apply person-centred ideals in my student involvements, work experiences and everyday life. In my current environment person-centred approaches are not commonly emphasised and I wish to learn more about applying person-centredness in my nursing practice. \nAim: To use self-reflection to describe how I have started to apply the principles of person-centredness to my experience as a nursing student, as a current healthcare provider and as a person. \nConclusion: Person-centredness and person-centred practice are complex but learning is continuous and the lessons learned can be applied in small ways in order to improve healthcare for practitioners and those receiving care. \nImplications for practice: \n•\tReaders can take this work and use it as an aid to examine their own experiences and how they relate to person-centredness \n•\tThis reflection could help others working in environments where person-centred approaches are not commonly emphasised to start developing their own person-centred care practices
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".