The predictive value of two on-site selection methods of undergraduate nursing students: A cohort study
Bibliographic record
Abstract
Nursing programs aim to select students who will succeed in theoretical studies and in clinical practice, and who are suitable for the profession. Recent literature has suggested an assessment of cognitive and non-cognitive skills in nursing student selection. The aim of this study is to compare the predictive value of two on-site selection methods used in nursing student selection, namely, psychological aptitude tests and literature-based exams. A cohort study was conducted. Students admitted to four undergraduate Bachelor of Science nursing programs at one Finnish nursing school between 2002 and 2004 (N = 626) were allocated into two cohorts based on the on-site selection method. Follow-up data was collected at two measurement points (May 2004–May 2009). The multimethod data collection included the use of admission archives (entrance exam scores), study records (study success) and a structured self-report questionnaire (knowledge and skills). Statistical data analysis was undertaken. According to the results, the two on-site selection methods produced very similar results regarding their predictive value. Both of the on-site selection methods predicted knowledge and skills, and study success of nursing students to some extent, but only explained a small proportion of variance. To conclude, neither of the two on-site selection methods should be used alone when predicting knowledge and skills or study success of nursing students. Further longitudinal research is needed to investigate the predictive value of various on-site selection methods.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".