Strengths and challenges associated with peer learning in a magnetic resonance imaging setting: A qualitative study
Bibliographic record
Abstract
Background and aim: Peer Learning is a pedagogical model in which students can share experiences and knowledge as well as reflect together. Research on Peer Learning has shown that when working together in pairs, the students can use and support each other to mirror and reflect on mutual insecurities. Peer Learning is a preceptorship model where two students are supervised by one preceptor. Considering the increasing shortage of radiographers, the method could be useful due to requiring fewer preceptors. The aim was to describe the strengths and challenges associated with a Peer Learning model in a magnetic resonance imaging setting from preceptors’ and undergraduate radiography students’ perspectives. Methods: Design: Qualitative design with an inductive approach. Settings: Radiology departments in seven hospitals in Sweden and the third year Bachelor radiography programme at a university in southern Sweden, during which students attend four weeks of clinical practice pertaining to the use of magnetic resonance imaging. Participants: 12 students and 14 preceptors were interviewed about peer learning in a magnetic resonance imaging setting. Methods: Focus group and individual interviews followed by conventional qualitative content analysis. Results: The results showed that students and preceptors were positive towards peer learning and saw many advantages. Preceptorship in Peer Learning required a significant amount of support and guidance from the radiology department. The students and preceptors indicated that they needed more education about peer learning. Conclusions: A prerequisite for the implementation of Peer Learning at clinical placements in a magnetic resonance imaging setting is education and training in peer learning for both students and preceptors, as well as support and guidance from universities and radiology departments.
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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 | 0.033 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".