Perceptions of midwifery students, mentors, and supervisors on the use of clinical teaching portfolio in Rwanda
Bibliographic record
Abstract
Background and objective: The clinical teaching portfolio is a practical tool for integrating theory and practice towards the growth of midwifery students. Both paper-based portfolios (PBP) and electronic portfolios (EP) are known and recognized as tools that can assist midwifery educators to develop students’ analytical and critical thinking. Hence, midwives are required to maintain a professional portfolio to reflect their development of knowledge, skills, and attitude. The aim of this study was to explore the perceptions of midwifery students, mentors, and supervisors about the use of paper-based and electronic clinical teaching portfolios.Methods: A qualitative descriptive design was carried out with 20 participants including midwifery students, mentors, and supervisors. Using purposive sampling, participants were recruited from two selected clinical teaching hospitals. All interviews were audio-recorded and transcribed verbatim by the research team members. Six steps of thematic analysis were followed during the inductive analysis of collected data. Results: Three themes emerged from data analyses which include clinical teaching and learning, encountering barriers in the use of the clinical teaching portfolio, and preference among the clinical teaching portfolio users.Conclusions: The findings of this study highlighted the importance of a clinical teaching portfolio in the promotion of midwifery clinical teaching. This study indicated that there is a need to respect the student-clinical instructor ratio and to equip the users with the required skills in order to fully benefit from the use of a clinical teaching portfolio.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".