Preparing Nursing Students to be Health Educators: Personal Knowing Through Performance and Feedback Workshops
Bibliographic record
Abstract
ABSTRACT Newly graduated RNs are expected to be competent health educators for individuals, groups, and communities. To prepare for this complex role, nursing students need time to focus on developing basic teaching skills and self-confidence in a non-threatening learning environment. Of primary importance to novice teachers’ development is taking the time to identify and appreciate the personal dimensions that are an integral part of the health educator role. Carper identified personal knowing as one of the four ways of knowing in nursing. This article describes an innovative praxis strategy that used videotaped performances, learner feedback, and self-reflection to encourage personal knowing in relation to the experience of nursing students learning to teach groups of clients. AUTHOR Received: May 14, 2004 Accepted: September 10, 2004 Ms. Little is an Instructor in the Collaboration for Academic Education in Nursing Program, Selkirk College, School of Health and Human Services, Castlegar, British Columbia, Canada. Address correspondence to Maureen Little, MScN, RN, Instructor, Collaboration for Academic Education in Nursing Program, Selkirk College, PO Box 1200, Castlegar, British Columbia, Canada V1N 3J1; e-mail: mlittle@selkirk.ca.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".