Bibliographic record
Abstract
Rural nursing is recognized as a unique health care domain. Within that context, the preceptorship experience is purported to be an important approach to preparing safe and competent rural practitioners. Preceptorship is the one-to-one pairing of a nursing student with a professional nurse who assumes the mandate of teacher and role model in a designated clinical/contextual setting, in this case the rural setting. A research gap exists in the literature in which rural preceptorship is specifically explored. The purpose of this paper is to review preceptorship in relation to preparing nursing students specifically for the rural setting. Understanding how preceptorship as an educational model can prepare nursing students to transition to rural practice is an important endeavor. An authentic rural preceptorship may serve to influence the recruitment and retention needs for registered nurses in rural areas. A greater understanding of rural preceptorship serves to illustrate the appropriate support, socialization and contextual competence required to prepare nursing students for rural nursing practice. This paper's review may serve to highlight the research that currently exists related to rural preceptorship and where additional research can contribute to further understanding and development for authentic rural nursing preparation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".