AC OMPREHENSIVE FACULTY DEVELOPMENT MODEL FOR NURSING EDUCATION MICHELE DRUMMOND-YOUNG, BSCN, MHSC (HCP), ⁎ BARBARA BROWN, BSCN, MSCN, ⁎ CHARLOTTE NOESGAARD, BN, MSCN, ⁎ OLA LUNYK-CHILD, BSCN, MSCN, ⁎
Bibliographic record
Abstract
Professional nursing education has undergone profound legislative changes requiring a university baccalaureate in nursing as entry to practice as a registered nurse (RN) in Ontario, Canada. Subsequent partnerships between colleges and universities were mandated by the ministry of post secondary education in order to maximize existing resources, such as faculty, and capitalize on the strenghts of both sectors. Faculty, in partnered collaborative undergraduate nursing programs, are challenged by the ever-evolving transition in conceptualization, development, and delivery of nursing education; consequently, the design, dissemination, and evaluation of effective faculty development programs is of paramount importance (Steinert, 2000). This paper focuses on the creation of the Comprehensive Faculty Development Model implemented by a collaborative BScN program partnership in south-western Ontario. It describes the model's contextual underpinnings, illustrates the component parts, explains their relationship, and provides an in-depth discussion of foundational concepts. The model was developed under the auspices of a collaborative faculty development committee with representation from all partners. Summaries of four research studies designed and implemented by members of the partnership provide a useful assessment of how faculty members experienced the inaugural BScN program; however, more study is needed in order to understand what approaches to faculty development are most effective and sustainable. (Index words: Faculty development; Faculty development model; Learning communities; Collaborative partnerships) J Prof Nurs
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".