An early patient contact programme combining simulation and real settings
Bibliographic record
Abstract
Context and setting We present the format and the first year's evaluation results of our Early Patient Contact Programme (EPCP) in the Year 2 curriculum. We implemented the EPCP during the 13-week ‘Introduction to Clinical and Pathological Sciences’ block. We used a portfolio-driven, multisetting-based, experiential learning approach to teach patient interviewing skills to students. Why the idea was necessary Although early student−patient contact is generally seen as an educationally sound approach, the hospital setting seems not to be optimal. Previously, there was no structured patient contact programme in our curriculum. Teaching took place on an ad hoc basis during the clinical years and there was neither content nor method consensus among teachers. What was done In the preparation phase, an educational collaboration contract was signed with 20 primary health care centres (PHCCs). A computerised standardised patient laboratory was established. Using guidelines drawn from the Calgary-Cambridge Observation Guide to the Consultation and MAAS-Global (an instrument designed to rate the communication and clinical skills of doctors in consultations), a conceptual framework and a set of patient interview guidelines were developed. A total of 284 Year 2 students were divided into 3 main rotating groups and then into 32 subgroups, each consisting of 3 students. Teaching teams were formed, each composed of 1 clinical teacher and 1 general practitioner (GP) teacher and each in charge of 3–6 subgroups. Nine teacher training and programme development workshops were organised for GPs and faculty members. A student learning and assessment portfolio was developed. In the implementation phase, the programme included 8 hours of preparatory lectures, 3 half-day work sessions at PHCCs and hospital clinics, 9 SP encounters (in 3 doctor and 6 observational roles) and 3 group feedback sessions. In each session, teachers worked with 3–6 students and used the experiential learning cycle. Students were given feedback on the SP contacts by their peers and the SPs following the SP contact and from their GP teachers through the records. Evaluation of results and impact The EPCP was evaluated by: student achievement: assessed with a portfolio (40%) and an SP-based performance examination (60%); a student feedback questionnaire; a student programme evaluation meeting, and teachers' SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Students' mean assessment score was 93.2 ± 5.5 out of a possible total of 100 points. The minimum score was 59.5. A total of 226 (79.6%) students scored > 90 points. The student questionnaire results showed the following student perceptions (% of ratings ≥ 7 on a 10-point scale): I can take an appropriate history (84.4%), and: I can conduct a complete clinical examination (81.0%). The average for the global rating item was 6.8 ± 2.1; that for learning at clinics and PHCCs was 6.5 ± 2.4. The highest rating score was for the SP sessions (7.5 ± 1.8). Similar positive feedback was given at the evaluation meetings. At the SWOT analysis, effective collaboration, bottom-up planning, teacher training programmes, the patient interview guidelines, teacher enthusiasm and student motivation were identified as strengths. The high number of students, time pressure, portfolio compilation procedure, and students' level of prior knowledge were identified as weaknesses. Our results demonstrate that in the early phase of the curriculum, an experiential learning-based, portfolio-driven EPCP using a combination of simulation, primary care and hospital clinics is feasible and is associated with high levels of student satisfaction and achievement.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".