Undergraduate research in nursing and health sciences: Curriculum design from first principles
Bibliographic record
Abstract
Background/Objective: Multidisciplinary learning within the framework of undergraduate education has recently been recognised as essential in developing an integrated and resilient healthcare system for the future. This inquiry seeks to derive common learning outcomes for a new multidisciplinary foundation research methods unit for undergraduate health sciences students. Methods: An outcomes-based design was used to determine the learning outcomes from first principles. All academics across multiple health disciplines at a regional university in Australia were invited to a series of meetings to brainstorm a set of common graduate capabilities and the scaffolds required to achieve them. Meetings were carefully documented and agreed to by consensus after member checking. A thematic analysis was undertaken to identify emergent themes. The capabilities themes were checked for alignment with the institutional graduate attributes and the thresholds of learning outcomes (TLOs) set out by the Australian Government Office of Learning and Teaching.Results: Three broad theoretical constructs emerged from the thematic analysis for the graduate capabilities: (i) health practitioners as evidence consumers (i.e. knowledge translation); (ii) health practitioners as evidence producers, (i.e. knowledge creation) and; (iii) ethical practice.Conclusions: This study derived a set of learning outcomes from first principles, while applying an outcomes-based curriculum design methodology. This may be a useful approach for finding common learning outcomes within a multidisciplinary health educational framework. Such structures and processes may not only help to provide students with a solid foundation for learning content that they have in common with other disciplines, but may also to facilitate interprofessional communication in future practice.
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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 | 0.086 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".