The “Five Minute Preceptor Model”: Development and evaluation of a training course for preceptors in nursing practical education in Austria: A pilot study
Bibliographic record
Abstract
Objective: The “Five Minute Preceptor Model” (5MP) is a teaching method which addresses the training needs of students within clinical placements. Investigation of its applicability for nursing education is equally missing as research on designing effective 5MP trainings for nurse preceptors. Aim of the pilot study was to develop and evaluate a 5 MP training and to assess its impact by measuring the utilization of the 5MP steps by the nurse preceptors.Methods: A quantitative design was used to evaluate the training directly after attendance, using descriptive statistics for data analyzes. The application of the 5MP steps was investigated before and six months after training using Wilcoxon test for statistical analyzes. A significance level of p < .05 was set. Comparative factor analysis was used to examine the 5MP model itself.Results: Participants (N = 92) overall rating of the trainings was high. The higher they rated the trainings the more they would applicate the 5MP in future preceptorship. Newsworthiness of the training was designated high but no difference was found in the application of the 5MP steps prior and after attendance of the training. Comparative factor analysis indicated that the 5MP steps were seen as more important after the training.Conclusions: The results suggest that the training is suitable for teaching nurse preceptors to use the 5MP. Although no significant differences were found in pre- and post-training usage, the comparative factor analysis shows increased knowledge through training attendance. Larger studies are needed to gain deeper insights into the 5MP model.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".