Midwifery Students and Obstetrical Residents Learning, Understanding and Application of Shared Decision Making
Bibliographic record
Abstract
Introduction: Childbirth is an important time in a client and patient’s life. The pregnant client seeks to obtain as much control over their circumstance as possible. The more perceived control in childbirth by the client, the better the outcomes are for the client-newborn dyad. One way that clients obtain control during childbirth is by participating in clinical decision making with their healthcare providers. This research intended to study the ways in which OB residents and midwifery students engaged in the understanding, learning and application of shared decision making with clients and patients. Methodology: This study utilised a constructivist grounded theory approach to obtain data and formulate a theory using semi-structured interviews with five senior obstetrical residents and five senior midwifery students from Ontario. Results: Qualitative data revealed four themes and eight sub-themes. Our theory describes the way residents and students absorb, mirror, and perform shared decision making through an informal process of observation and experience throughout their training. Our theory further describes how support for students and residents creates the foundation for learning shared decision making. Support includes how the mentor minimizes the impacts of the hierarchy of power in medical and midwifery education, as well as increasing psychological safety for the learner. Conclusion: The study results support the exploration of future methods for the teaching of shared decision making to obstetrical residents and midwifery students. Participants of this study agreed that more training is needed for shared decision making, as well as training for the mentor to ensure learners are optimizing their experience. More training needs to be available for mentors to help reduce the negative impacts of the hierarchy of power, and to increase psychological safety for the learner.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".