A problem-based learning approach to midwifery
Bibliographic record
Abstract
In 1993, McMaster University implemented the first midwifery education programme in Ontario, Canada. A 4–year, direct-entry baccalaureate programme was established and reflects the philosophy of midwifery with a focus on normal, healthy childbearing. Self-directed and problem-based learning (PBL) are integral parts of the programme. The authors selected a PBL format because of the benefits demonstrated in medical education. The use of PBL, as a method of instruction, has been found to enhance clinical reasoning skills, knowledge acquisition and self-directed learning patterns (Woods, 1994). A PBL curricula also provides students with an earlier opportunity to acquire information in context (Woodward, 1989). Students from a traditional curricula, compared with students from a PBL curricula, have been found to be less able to use what they have learned (Gonella et al, 1970). The development and implementation of a PBL midwifery curricula are described in this paper. Graduates of the programme reported that they enjoyed the small group tutorials and found it to be one of the most effective aspects of learning in the programme, second only to clinical placements.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".