EXPLORING PUBLIC HEALTH NURSE PRECEPTORS ’ EXPERIENCE OF LEARNING
Bibliographic record
Abstract
Exploring Public Health Nurse Preceptors’ Experience of Learning\nThe preceptorship model is the leading approach to clinical teaching in undergraduate nursing programs. There is a need for community placements however a lack of preceptors. In preceptor-student relationships experienced nurses learn along with the students. This qualitative study utilized hermeneutic phenomenology to answer the questions: How do public health nurse preceptors experience learning within a preceptor-student relationship? What is the meaning of learning for public health nurse preceptors Seven public health nurse preceptors were recruited from health units in Ontario. Findings from this study reveal the tacit knowledge within experienced nurse preceptors. Preceptors learned from their preceptee, explored similarities and differences and were challenged by uncertainties in their practice. Preceptors experienced tensions between holding on and letting go, between work and home life, and within the ‘swamp’ of practice. This study reveals the experiential tacit knowledge, practical wisdom, reciprocal learning and professional development of nurses within preceptorships.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".