Multi-mini-interviews for nurse recruitment
Bibliographic record
Abstract
The effectiveness of traditional interviews in assessing candidates’ suitability for nursing has been called into question (Perkins et al, 2013; Rodgers et al, 2013). There is also a risk of interview bias or chance (Eva et al, 2009). The use of the multiple mini interview (MMI) to select candidates was pioneered in Canada; it is now used in many parts of the world. Several studies have evaluated the MMI’s feasibility, validity and effectiveness in determining the suitability of applicants and their subsequent clinical performance (Dowell et al, 2012; Eva et al, 2009; 2004; Reiter et al, 2007). Its success as an indicator of the professionalism and communication skills of candidates and its cost-effectiveness in terms of interview time have also been studied (Eva et al, 2009; Rosenfeld et al, 2008).
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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 | 0.066 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.113 | 0.042 |
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".