Integrating the scholarship of practice into the nurse academician portfolio
Bibliographic record
Abstract
Many nurse academicians are also advanced practice nurses (APNs) in the United States (U.S.). Such faculty are often involved in clinical practice activities which require specific competencies for meeting legal responsibilities and standards for safety. Maintaining practice expectations often takes away the time necessary to address traditional scholarship expected by U.S. institutions for advancement and promotion, and securing tenured academic positions. Scholarship activities for APNs should encompass broad and essential criteria during review and promotional processes; especially important is documentation for the scholarship of application as it applies to APN clinical practice. Nursing faculty who practice as APNs should become adept at documenting scholarship of practice. Appropriate recognition for valid non-traditional types of scholarship activities is essential. According to the Position Statement of the American Association of the Colleges of Nursing (AACN) on Defining Scholarship for the Discipline of Nursing, there are four different dimensions of scholarship. These expand upon the seminal work of Boyer (1990) and consist of: (a) the Scholarship of Discovery, (b) the Scholarship of Teaching, (c) the Scholarship of Application, and (d) the Scholarship of Integration. The purpose of this article is to describe each of the four types of scholarship and demonstrate how the APN/ nurse academician’s clinical practice is an application of scholarship. APN practice is multidimensional and an important basis for review and promotion in academic and/or health care settings.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.002 | 0.027 |
| 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".