A randomized matched-pairs study evaluating a hybrid, structured skills course for clinical officers in Tanga, Tanzania
Bibliographic record
Abstract
Background A hybrid training programme for the Fundamental Interventions, Referral and Safe Transfer (FIRST) course was conducted because of the COVID-19 pandemic to prepare clinical officer students for the FIRST OSCE. The course occurred in Tanzania with in-person instruction, while a Canadian team lectured remotely. This study determined the effectiveness of the hybrid course by comparing OSCE performance between students who did and did not take the course. Student and instructor feedback on the virtual portions of the FIRST course were also evaluated. Methods Clinical officer students were matched in pairs based on age, sex, work experience, and school performance. One student from each pair was assigned to take the hybrid course (intervention), while the other did not (control). Both groups of students took the OSCE, and their performance was scored. The FIRST course was provided to the control group after the OSCE. Both groups then completed precourse and postcourse surveys to identify successes and challenges with conducting the course. Analyses were based on descriptive statistics, as well as paired t-test and Wilcoxon signed-rank test analysis. Results The 22 students in the intervention group outperformed the 22 control students (P<0.001). The intervention group’s mean score was 39/50, compared with 27/50 for the control group. The hybrid FIRST course successfully prepared students for all OSCE skills. There was no difference between the 2 groups in terms of performance on written quizzes. The survey response rate was 77%. Almost all students (94%) believed the course would help them provide better patient-centred care. Overall, 88% of the students recommended that the college continue teaching the hybrid FIRST course. Conclusions COVID-19 has prompted adaptation. We demonstrated that hybrid courses are feasible and effective strategies for providing future clinicians with the skills needed for patient-centred care, during and potentially even after the resolution of the COVID-19 pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".