Facilitating Formative Feedback in Midwifery Education: A Narrative Review
Bibliographic record
Abstract
Background: Effective formative feedback is an important educational intervention in clinical learning. Receiving formative feedback enhances knowledge and skill acquisition and promotes reflective practice. The provision of feedback is a critical component of this education and requires a bidirectional process between learner and preceptor. Despite this critical role in health profession learning, there has been limited exploration of how preceptors provide feedback in the Ontario midwifery education setting. Aim: To determine strategies for how formative feedback could be provided in the Ontario midwifery education setting to maximize students’ clinical learning. Methods: We conducted a narrative literature review using PubMed, Ovid MEDLINE, and CINAHL databases. Following our initial search, each title and abstract was assessed for inclusion for full text review. The final data set was reviewed and coded in order to undertake a descriptive thematic analysis. Findings: There is little Ontario-specific midwifery feedback literature. Thematic analysis identified that understanding best practices for feedback, preparing both student and preceptor for a feedback relationship, and using a written format and a standardized assessment tool for feedback are strategies that can optimize learning in the clinical setting. The need for improved formative feedback provision has been identified in other midwifery jurisdictions, resulting in the introduction of workplace-based assessment tools to provide structured, high-quality feedback. The introduction of such a tool, specifically the midwifery mini-clinical evaluation exercise tool, may promote improved learning for students. This article has been peer reviewed.
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 | 0.022 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".