Responding to the real problem of sustainable resuscitation skills with real assessment. Mixed‐methods evaluation of an authentic assessment programme
Bibliographic record
Abstract
INTRODUCTION: The retention of resuscitation skills is a widespread concern, with a rapid decay in competence frequently following training. Meanwhile, training programmes continue to be disconnected with real-world expectations and assessment designs remain in conflict with the evidence for sustainable learning. This study aimed to evaluate a programmatic assessment pedagogy which employed entrustment decision and the principles of authentic and sustainable assessment (SA). METHODS: We conducted a prospective sequential explanatory mixed methods study to understand and address the sustainable learning challenges faced by final-year undergraduate paramedic students. We introduced a programme of five authentic assessments based on actual resuscitation cases, each integrating contextual elements that featured in these real-life events. The student-tutor consensus assessment (STCA) tool was configured to accommodate an entrustment scale framework. Each test produced dual student led and assessor scores. Students and assessors were surveyed about their experiences with the assessment methodologies and asked to evaluate the programme using the Ottawa Good Assessment Criteria. RESULTS: Eighty-four students participated in five assessments, generating dual assessor-only and student-led results. There was a reported mean score increase of 9% across the five tests and an 18% reduction in borderline or below scores. No statistical significance was observed among the scores from eight assessors across 420 unique tests. The mean student consensus remained above 91% in all 420 tests. Both student and assessor participant groups expressed broad agreement that the Ottawa criteria were well-represented in the design, and they shared their preference for the authentic methodology over traditional approaches. CONCLUSION: In addition to confirming local sustainability issues, this study has highlighted the validity concerns that exist with conventional resuscitation training designs. We have successfully demonstrated an alternative pedagogy which responds to these concerns, and which embodies the principles of SA, quality in assessment practice, and the real-world expectations of professionals.
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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 | 0.052 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".