Nurse anesthesia student’s personality characteristics and academic performance: A big five personality model perspective
Bibliographic record
Abstract
Purpose: The purpose of this correlational research was to assess the association between the Five Factor Model (FFM) of personality traits and the grade point average (GPA) of students enrolled in a nurse anesthesia program (NAP). This research was conducted to identify a more objective way to evaluate a prospective students’ personality that correlates with academic success in a NAP.Methods: The Program Directors of students enrolled in NAPs throughout the United States were randomly contacted to assist with the data collection. The FFM of personality traits inventory and question requesting the student to self-report their GPA were then forwarded to the student.Results: Upon completion, the data was analyzed using SPSS 22.0. The results demonstrated a significant correlation, indicating that the dimensions of personality were significantly related to GPA. The dimensions of personality accounted for 12% of the variance in GPA. An examination of individual regression coefficients revealed that the personality trait, conscientiousness, was significantly and positively related to GPA.Conclusions: Nurse anesthesia admission committees can use this personality inventory to better guide them in selecting candidates for their program. Used in conjunction with the more traditional admissions criteria; NAPs can be better positioned to select students more objectively that will be successful in their program, hence leading to lower attrition rates.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".