Assessment of non-clinical attributes in paramedicine using multiple mini-interviews
Bibliographic record
Abstract
BACKGROUND: Non-clinical attributes are increasingly emphasised as an important factor in paramedic practice. However, the assessment of these attributes often lacks the evidence base to support it. Exploring the relationship between non-clinical attributes and clinical skills is also of theoretical and practical importance. OBJECTIVE: To first seek evidence of reliability and validity for the assessment of non-clinical attributes using the multiple mini-interview (MMI) in paramedic contexts and second, to explore the association between non-clinical attributes and clinical skills in paramedicine. METHODS: Entry to practice level paramedic candidates completed a 10-station MMI to assess non-clinical attributes on day 1 and a 10-station simulation-based assessment (SBA) of clinical skills on day 2. Both were assessed using different global rating scales. Our primary outcomes included MMI inter-station reliability (calculated using generalisability theory) and Pearson's correlation between non-clinical attributes and clinically focused skills. RESULTS: 30 trainees completed the MMI and 26 of the 30 completed the SBA. Inter-station reliability for the MMI reached 0.77. Pearson's correlations (disattenuated correlations in parentheses) between the overall MMI score and mean SBA global rating scores reached r=0.31 (r=0.48) and ranged by dimension from r=-0.11 (-0.17) (procedural skills) to r=0.54(r=0.83) (communication). CONCLUSIONS: The MMI demonstrated evidence of reliability and validity for the assessment of non-clinical attributes in paramedic contexts. Non-clinical attributes and paramedic clinical skills are likely distinct but related constructs, each contributing to the construct of paramedic practice. Programmes of assessment should include both to ensure the construct of paramedic practice is adequately represented.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.269 | 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".