Post graduate clinical placements: evaluating benefits and challenges with a mixed methods cross sectional design
Bibliographic record
Abstract
BACKGROUND: Systematic evaluations of clinical placements are rare, especially when offered alongside academic postgraduate courses. An evidence-based approach is important to allow pedagogically-driven provision, rather than that solely governed by opinion or market demand. Our evaluation assessed a voluntary clinical placement scheme allied to a mental health course. METHODS: Data were collected over academic years 2010/11- 2013/14, from participating students (n = 20 to 58) and clinician supervisors (n = 10-12), using a mixed-methods cross-sectional design. Quantitative evaluation captured information on uptake, dropout, resource use, attitudes and experience, using standardized (the Placement Evaluation Questionnaire; the Scale To Assess the Therapeutic Relationship - Clinical version and the University of Toronto Placement Supervisor Evaluation) and bespoke questionnaires and audit data. Qualitative evaluation comprised two focus groups (5 clinicians, 5 students), to investigate attitudes, experience, perceived benefits, disadvantages and desired future developments. Data were analysed using framework analysis to identify a priori and emergent themes. RESULTS: High uptake (around 70 placements per annum), low dropout (2-3 students per annum; 5 %) and positive focus group comments suggested placements successfully provided added value and catered sufficiently to student demand. Students' responses confirmed that placements met expectations and the perception of benefit remained after completion with 70 % (n = 14) reporting an overall positive experience, 75 % (n = 15) reporting a pleasant learning experience, 60 % (n = 12) feeling that their clinical skills were enhanced and 85 % (n = 17) believing that it would benefit other students. Placements contributed the equivalent of seven full time unskilled posts per annum to local health care services. While qualitative data revealed perceived 'mutual benefit' for both students and clinicians, this was qualified by the inherent limitations of students' time and expertise. Areas for development included fostering learning around professionalism and students' confidence on placement. CONCLUSIONS: The addition of healthcare placements to academic postgraduate taught courses can improve their attractiveness to applicants, benefit healthcare services and enhance students' perception of their learning experiences. Well-positioned and supported placement learning opportunities could become a key differentiator for academic courses, over potential competitors. However, the actual implications for student employability and achievement remain to be established.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".