113COMMUNITY PLACEMENTS IN FOUNDATION YEAR 1: MAXIMISING POTENTIAL
Bibliographic record
Abstract
To expand community experiences for foundation year one (FY1) trainees, in line with Health Education England ‘Broadening the Foundation Programme’ initiatives [1], the medicine for older persons unit at St Thomas’ Hospital has created an innovative placement allocating two weeks per trainee in a community-facing setting during their rotation. This includes work in the Acute Frailty Unit, with the ‘@home team’ (a hospital at home service provided in Southwark and Lambeth), within a local continuing care unit, and alongside integrated community therapy teams within local clinics. Feedback from the cohort of 15 FY1s from 2015–2016 who completed the placement was collected as part of a quality improvement initiative. They were also asked for an overall rating out of 10, with 1 being the worst score and 10 being the best score. 53% FY1s felt like a ‘wasted resource’, or ‘medical student’, as the work was often consultant-led, and the workload much less than their usual ward-based workload. 67% suggested that they wanted additional clinic time, as clerking patients independently gave them confidence and autonomy. The average overall score for the placement was 4.6/10. There were clear recommendations for change, including but not limited to: a comprehensive induction with clearly defined roles for the FY1, specific learning outcomes for the two week community placement as guided by the foundation programme curriculum, more time within integrated community therapy clinics and/or care homes, and scheduled consultant led teaching time. Negative attitudes towards community placements are relatively common, with perceptions that placements are ‘easy’, the hours are reduced and there are fewer learning outcomes [1]. Evidence from Health Education England suggests changed attitudes following placements, with nearly unanimous views that community placements are beneficial [1]. Our study shows that this is not always the case, and highlights improvements which can be made to current programmes to ensure that community placements are beneficial to FY1 training. Following this study we introduced a more comprehensive induction process, with clear learning objectives, and reorganised the timetable to enable a placement tailored to specific FY1 interests. With a lack of data available regarding community-facing placements, and an increasing focus on integrated care and recruitment into the community, our study adds much needed insight into ways to maximise the clinical effectiveness and potential educational benefits of such placements.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".