Translation and further validation of a global rating scale for the assessment of clinical competence in prehospital emergency care
Bibliographic record
Abstract
Global rating scales are useful to assess clinical competence at a general level based on specific word dimensions. The aim of this study was to translate and culturally adapt the Paramedic Global Rating Scale, and to contribute validity evidence and instrument usefulness in training results and clinical competence assessments of students undergoing training to become ambulance nurses and paramedics at Swedish and Finnish universities. The study included translation, expert review and inter-rater reliability (IRR) tests. The scale was translated and culturally adapted to clinical and educational settings in both countries. A content validity index (CVI) was calculated using eight experts. IRR tests were performed with five registered nurses working as university lecturers, and with six clinicians working as ambulance nurses. They individually rated the same simulated ambulance assignment. Based on the ratings IRR was calculated with intra-class correlation (ICC). The scale showed excellent CVI for items and scale. The ICC indicated substantial agreement in the group of lecturers and a high degree of agreement in the group of clinicians. This study provides validity evidence for a Swedish version of the scale, supporting its use in measuring clinical competence among students undergoing training to become ambulance nurses and paramedics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".