Bibliographic record
Abstract
Since the professions moved into higher education, diversity has developed in the amount, depth and method of bioscience teaching in nursing and midwifery courses. Bioscience encompasses biology, life science, anatomy and physiology. This diversity is a cause for concern at a time when nurses and midwives are taking on more of the traditional medical tasks such as prescribing and running clinics. Students need to acquire a sound grasp of anatomy and physiology and to achieve this a substantial amount of curriculum time needs to be devoted to bioscience. The main argument concerns not what should be taught but who should teach bioscience to students; whether this should be specialist lecturers from higher education science departments or nursing and midwifery teachers from health studies. This article makes the case for collaboration involving subject specialists and nursing/midwifery teachers and this is illustrated by examples of how such collaboration works in one higher education institution to produce a practical laboratory-based course. The conclusion is that time spent in life science laboratories should not be considered a waste of nursing/midwifery teaching time because the life science laboratory is a microcosm of clinical practice. This relevance can be emphasised through collaboration between nursing and bioscience lecturers.
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 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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".