Psychiatric-mental health nurse practitioner student preceptorship: Perspectives on the clinical training
Bibliographic record
Abstract
As today’s health-service delivery continues to evolve and transform, keeping pace with the training needs of professionals entrusted to deliver quality, competent care is itself an evolutionary and multifaceted academic undertaking. In the United States, psychiatric mental health nurse practitioners (PMHNPs) have been evidenced as formative, effective, and necessary contributors to quality, cost- effective patient, family, and community-based mental health care across the lifespan. The education and certification processes for PMHNPs involve a comprehensive and rigorous combination of theoretical course-work and clinical practicum guided by the concepts and principles of the preceptorship model. The purpose of this paper is to use the available literature to discuss and gain insights into some clinical and educational perspectives influencing PMHNP students’ practice preparations within the context of the preceptorship-paradigmatic relationship. Along with adding to the literature, this paper could have important implications from the standpoint of the PMHNP student-faculty-preceptor 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".