Correlation of formative assessments as the means of predicting summative performance in paramedic students
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Bibliographic record
Abstract
Background: Paramedic programs use formative assessments to determine cognitive competency. Understanding the number of failed formative units as a probability of passing the summative exam will allow programs to set additional benchmarks. The purpose of this study was to determine whether failure in formative exams determines success on a summative exam. Methods: Formative and summative scores from 2011 – 2016 for paramedic students with accounts in Fisdap™, an Internet-based administrative database, were retrospectively reviewed for the following criteria: provided consent for research, completed all six formative (unit) examinations, and completed a summative (comprehensive) examination. Analyses were performed with Pearson correlations and linear regression. Results: A total of 1,406 student records were included based on inclusion criteria. Correlation with each formative and the summative examination were all significant, p < 0.001: Cardiology 0.597; Airway 0.571; Medical 0.571; Trauma 0.566; Ob/Pediatrics 0.549; Operations 0.495. The cardiology exam was shown to have a moderate correlation on summative performance, whereas the operations exam had the weakest correlation. The number of formative examination failures was a significant predictor of the probability of passing the summative examination, t(1405) = –31.02, p < 0.001. Zero failed unit examinations yielded a 100% probability of passing. Three failed formative exams yielded a 60.4% probability. Four failed attempts yielded a 44.8% probability. Failure of all six formative exams yielded a 13.4% probability of passing the Paramedic Readiness Exam Version 3. Conclusion: Not all formative examinations hold the same predictive power on the probability of passing a summative examination. Each had their own correlation value. Students who did not fail formative examinations have a 100% likelihood of passing the summative examination.
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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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it