Putting the Evidence into Preceptor Preparation
Bibliographic record
Abstract
The term evidence-based practice refers to the utilization of knowledge derived from research. Nursing practice, however, is not limited to clinical practice but also encompasses nursing education. It is, therefore, equally important that teaching preparation is derived from evidence also. The purpose of this study was to examine whether an evidence-based approach to preceptor preparation influenced preceptors in a assuming that role. A qualitative method using semistructured interviews was used to collect data. A total of 29 preceptors were interviewed. Constant comparative analysis facilitated examination of the data. Findings indicate that preceptors were afforded an opportunity to participate in a preparatory process that was engaging, enriching, and critically reflective/reflexive. This study has generated empirical evidence that can (a) contribute substantively to effective preceptor preparation, (b) promote best teaching practices in the clinical setting, and (c) enhance the preceptorship experience for nursing students.
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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 | 0.193 | 0.402 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".