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Record W2019625273 · doi:10.5430/jnep.v2n3p54

Using Boyer to create a culture of scholarship: Outcomes from a faculty development program

2012· article· en· W2019625273 on OpenAlexvenueno aff
Maryann O. Forbes, Jane H. White

Bibliographic record

VenueJournal of Nursing Education and Practice · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipEconomic shortagePromotion (chess)Medical educationFaculty developmentMedicineSociologyPolitical sciencePsychologyProfessional developmentGovernment (linguistics)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.542
GPT teacher head0.625
Teacher spread0.083 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

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