A preceptorship model to facilitate clinical nursing education in health training institutions in Botswana
Bibliographic record
Abstract
BACKGROUND: Despite the wide use of preceptorship, there is evidence that preceptorship and the role of preceptor in clinical nursing education are not clearly understood or supported. OBJECTIVES: To develop a preceptorship model to facilitate clinical nursing education in Botswana. METHOD: The model development in this study followed the steps of theory generation as described by Chinn and Kramer. These four steps are concept analysis, relationship statements, description and critical reflection of the model. RESULTS: Four main themes emerged from the empirical study that formed the basis for key concepts and model development. The model has six components, namely, agent, recipient, context, procedure, dynamics and terminus. The description of the model is based on Chinn and Kramer. CONCLUSION: The need for a preceptorship model to facilitate preceptorship cannot be overemphasised in this regard. This model will guide the planning and implementation of preceptorship procedures by different stakeholders to improve its effectiveness in clinical nursing education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".