Novel clinical learning from a student‐led clinic
Bibliographic record
Abstract
BACKGROUND: Student-led clinics have the potential to meet community rehabilitation needs and provide valuable clinical placement experiences. Student-led clinical learning may offer pedagogical advantages similar to problem-based learning; however, this line of research is new and little is known about student-led clinical learning. The purpose of this study was to describe the novel learning experiences of a clinical placement in a student-led clinic from student perspectives. METHODS: We conducted a descriptive qualitative focus group study with six participants. The focus group began with an open-ended question: 'What does the student-led clinic mean to you as a learner?' This was followed by a discussion of three topics: (1) the student-led clinic is learner focused; (2) faculty involvement in supervision; and (3) learning in a shared space with other students. A content analysis was used to analyse the data, and a summary of the researcher's interpretations was sent to study participants for further comments. RESULTS: There were two major themes that represented experiences within the student-led clinic. 'Increased responsibility' was viewed as a novel experience, and as reducing the perceived gap between the classroom and the 'real world'. As clinical instructors do not carry their own caseload, the second theme pertained to 'Safety-in-learning'. Although this theme was not viewed as novel, it was perceived to be important in enabling autonomy. Increased responsibility' was viewed and as reducing the perceived gap between the classroom and the 'real world CONCLUSION: Student-led clinics have clear community benefits by mitigating service gaps while providing experiential learning opportunities. These learning opportunities are perceived to be novel, and may resemble advantages observed in the classroom with problem-based learning.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".