Script concordance testing to understand the hypothesis processes of undergraduate nursing students - Multiple case study
Bibliographic record
Abstract
Background: Albeit essential to clinical reasoning (CR), strategies for generating student nursing clinical hypotheses at the time of transition to professional practice are underdeveloped. While script concordance testing (SCT) has been shown to be a valid and reliable assessment tool for CR in nursing education, the thought processes including the hypothesis processes involved in choosing an answer is not examined.Methods: A multiple case study was used to understand the complex phenomenon of students’ hypothesis activation and confrontation with the combined use of SCT questions and the think-aloud method. Structured individual interviews were conducted.Results: A total of 18 students, nine first-year and nine third-year students participated in the study. The results show that the students demonstrate certain CR cognitive processes, including early representation of a clinical situation, semantic transformation of data, and hypothesis comparison.Conclusions: Results suggest promoting knowledge articulation aloud and the frequent use of micro-judgments to compare and differentiate hypotheses involving the uncertainty of clinical practice, which underpin learning in successive layers.
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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 | 0.034 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".