Becoming an interprofessional practitioner: factors promoting the application of pre-qualification learning to professional practice in maternity care
Bibliographic record
Abstract
Teamwork and collaboration have been recognized as essential competencies for health care providers in the field of maternity care. Health care policy and regulatory bodies have stressed the importance of Interprofessional Education (IPE) for learners in this field; however, there is little evidence of sustained application of pre-qualifying IPE to the realm of interprofessional collaboration (IPC) in practice following qualification. The aim of this research was to understand how newly qualified midwives applied their IPE training to professional practice. A purposive sample of midwifery students, educators, new midwives and Heads of Midwifery from four universities in the United Kingdom participated in semi-structured interviews, questionnaires and focus groups. Qualitative, grounded theory methodology was used to develop the emerging theory. Newly qualified midwives appeared better able to integrate their IPE training into practice when IPE occurred in a favourable learning environment that facilitated acquisition and application of IPE skills and that recognized the importance of shared partnership between the university and the clinical workplace.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".