The Medical Training Evaluation Questionnaire (MeTrE-Q): a multidimensional self-report instrument for assessing the quality of midwifery students' education
Bibliographic record
Abstract
This study aims to understand the factors that can hinder Italian midwifery students' educational process, what messages are given to students during their clinical practice, and how students interact with tutors and other professionals. Seven hundred and eighty Italian students of midwifery were asked to answer an Internet-based questionnaire regarding their own opinions concerning their theoretical–practical formative path. For male Italian students, satisfaction is lower than female students as well as for students from Southern than Northern Italy. Students are dissatisfied with the quality of their academic and practical training, particularly regarding recognising their professional role and their relationship with tutors. Based on these data, it is essential to design a formative path for midwives that considers students' opinions and the positive experiences of other countries.Impact StatementWhat is already known on this subject? Several studies underscore the poor preparation of students for learning in clinical settings. The current reality of the Italian academic path in most universities disregards midwifery students' expectations and formative needs.What do the results of this study add? For male Italian students, satisfaction is lower than for female students and students from Southern than Northern Italy. Students are dissatisfied with the quality of their academic and practical training, particularly regarding recognising their professional role and their relationship with tutors.What are the implications of these findings for clinical practice and/or further research? It is essential to design a formative path for midwives that considers students' opinions and other countries' positive experiences.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".