Using Boyer to create a culture of scholarship: Outcomes from a faculty development program
Bibliographic record
Abstract
Background: The current nursing faculty shortage has created an urgent need to retain and mentor novice faculty members. Programs to foster faculty development related to scholarship should be part of a school’s overall strategic plan. Methods: This article presents a description of a faculty development program with outcomes regarding scholarship for a five year period. The Boyer model of scholarship was used as a framework to assess resources and also served as a guide for program development. The program was tailored to faculty needs and targeted specific aspects of Boyer’s domains; strategies were developed which promoted faculty growth in these areas. Specific activities were planned and implemented to accomplish the overall outcome of increasing faculty scholarship and create a research culture in the school. Results: The scholarship of discovery was a major area identified for faculty growth. The program goal of increasing faculty scholarship was achieved by an increase in the number of presentations, publications and funded projects for the faculty in the school over a five year period. Conclusions: Using the Boyer model as a framework assisted faculty in achieving a more positive view of their scholarship, facilitated preparation of meaningful dossiers for promotion and tenure decisions, and contributed to knowledge development in the discipline.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".