Peer assisted learning in the clinical setting: an activity systems analysis
Bibliographic record
Abstract
Peer assisted learning (PAL) is a common feature of medical education. Understanding of PAL has been based on processes and outcomes in controlled settings, such as clinical skills labs. PAL in the clinical setting, a complex learning environment, requires fresh evaluation. Socio-cultural theory is proposed as a means to understand educational interventions in ways that are practical and meaningful. We describe the evaluation of a PAL intervention, introduced to support students' transition into full time clinical attachments, using activity theory and activity systems analysis (ASA). Our research question was How does PAL transfer to the clinical environment? Junior students on their first clinical attachments undertook a weekly same-level, reciprocal PAL activity. Qualitative data was collected after each session, and focus groups (n = 3) were held on completion. Data was analysed using ASA. ASA revealed two competing activity systems on clinical attachment; Learning from Experts, which students saw as the primary function of the attachment and Learning with Peers, the PAL intervention. The latter took time from the first and was in tension with it. Tensions arose from student beliefs about how learning takes place in clinical settings, and the importance of social relationships, leading to variable engagement with PAL. Differing perspectives within the group were opportunities for expansive learning. PAL in the clinical environment presents challenges specific to that context. Using ASA helped to describe student activity on clinical attachment and to highlight tensions and contradictions relating PAL in that setting. Planning learning opportunities on clinical placements, must take account of how students learn in workplaces, and the complexity of the multiple competing activity systems related to learning and social activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".