Securing preceptors for advanced practice students
Bibliographic record
Abstract
This paper discusses challenging issues and guidelines for securing preceptorships for advanced practice registered nurse (APRN) students, i.e., clinical nurse specialists, nurse midwives, nurse practitioners, and nurse anesthetists. Student preceptorships are facilitated when there is a relationship that fosters collaboration and cooperation between faculty members in academic institutions and preceptors in clinical institutions. The faculty member in the academic setting ensures that requirements for the course and clinical experiences can be met through the choice of preceptor and patient population or setting within a clinical institution. Faculty must identify, select and contract with preceptors who are not only clinical experts, but who are able to function effectively in the roles of coach and mentor for the advanced practice nursing student. Consideration of the curriculum, student background and preceptor characteristics allows faculty to tailor the clinical assignment so that course and clinical outcomes and student goals are achieved. Faculty, preceptor and student engage in a dialog that delineates responsibilities for orientation, course and clinical expectations, supervision and evaluation. Securing written contracts and clarifying responsibilities is the result of a collaborative relationship that confirms the commitment of each partner in the preceptorship.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".