The benefits and challenges of student-led clinics within an Irish context
Bibliographic record
Abstract
Student-led clinics are being established internationally as a means of practice education within a variety of disciplines. These clinics can provide opportunities for students in health care professions to have ‘real life’ clinic experiences while also providing beneficial outcomes for service users. This paper reviews the preliminary experiences from thirteen uni-disciplinary student-led clinics (thirty two students in total) in the disciplines of Occupational Therapy (OT), Speech and Language Therapy (SLT) and Physiotherapy (PT). These clinics were part of the placement experience of the students in an Irish University between 2011 and 2013. Clinical Education Quality Audit (CEQA) questionnaires (Ladyshewsky & Barrie, 1996) were used to explore the student experience of these placements, and practice educators were given an opportunity to discuss the benefits and challenges of the placements with the University Practice Education team. The data collected was analysed using thematic analysis. A number of themes emerged from the data: Environment, Organisational issues, Professional development / growth, and Relationships. These themes highlighted both positive and challenging features of the placement experiences. This paper will discuss the benefits and challenges of these student-led clinics and outline that overcoming challenges may be an additional important aspect of learning in innovative clinical experiences
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".