A systematic review protocol on the use of online learning versus blended learning for teaching clinical skills to undergraduate health professional students
Bibliographic record
Abstract
Aim: This paper is a review protocol that will be used to identify, critically appraise and synthesise the best current evidence relating to the use of online learning and blended learning approaches in teaching clinical skills in undergraduate health professionals. Background: Although previous systematic reviews on online learning vs. face to face learning have been undertaken a systematic review on the impact of online learning and blended learning for teaching clinical skills has yet to be considered in undergraduate health professionals. By reviewing the students’ online learning experiences, systems can potentially be designed to ensure all health professional students’ are supported appropriately to meet their learning needs. Methods/design: The key objectives of the review are to evaluate how online learning teaching strategies assist students learn; to evaluate the students’ satisfaction with this form of teaching; to explore the variety of online learning strategies used; to determine what online learning strategies are more effective and to determine if supplementary face to face instruction enhances learning. A search of the following databases will be made MEDLINE, CINAHL, BREI, ERIC and AUEI. This review will follow the Joanna Briggs Institute guidance for systematic reviews of quantitative and qualitative research. Conclusion: This systematic review protocol intends to support the undertaking of a systematic literature review which will report on a combination of student experience and learning outcomes therefore increasing its utility for educators and curriculum developers involved in health care education.
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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 | 0.169 | 0.149 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.012 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".