The Significance of Preplanning and Faculty Engagement in Curriculum Change
Bibliographic record
Abstract
The challenge of curriculum renewal in nursing is ensuring a balance of rigor with a flexible, robust evidence-informed curriculum.To achieve this, the faculty at Dalhousie University School of Nursing used a unique and creative approach to develop a new nursing curriculum.Extensive preplanning, utilization of small working groups, working through consensus building, and utilizing a project plan engaged faculty in all facets of the curriculum development.Draft plans were developed which were reviewed and revised by all faculty through multiple creative planning events.This process allowed consensus around key decisions such as the philosophical underpinning of the curriculum, core themes, and new educational approaches.Using this framework, coupled with preplanning and data collection before starting the curriculum revision process allowed faculty to have a Senate-approved new nursing curriculum in about 18 months from initial discussions and resulted in high levels of faculty engagement.
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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 | 0.182 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".