Impact of the foundation program and high school grade in predicting the cumulative grade point average (CGPA) among the graduates of the nursing program at the Ministry of Health Educational Institutes in Oman
Bibliographic record
Abstract
Background and objective: Higher education has witnessed significant changes in order to provide quality education that meets the needs of the 21st century. To be in par with international best practices, the foundation program was established to prepare the high school leavers for higher education in Oman, a middle-eastern country. The aims of this study were to (1) assess the relationship between students’ high school scores and their cumulative grade point average (CGPA) among the graduates of the nursing program in Oman, and (2) compare the CGPA of the student who attended the general foundation program (GFP) compared to the ones who did not attend the GFP. Methods: Secondary data analysis approach was used to access the alumni files one year before and one year after the implementation of the GFP. A retrospective approach was used to gather data from the alumni files, which included high school grade, whether the graduates have attended the foundation program or not, their first year Grade point average (GPA), and their CGPA.Results: Six hundred twenty-seven (n = 627) graduates were recruited from two cohorts, one attended the GFP (n = 287; 45.8%) and the others did not attend the GFP (n = 340; 54.2%). Majority of the participants who were included in this study were female graduate (n = 535; 85.3%). The students who attended the GFP were found to have higher first year GPA and higher CGPA compared to those who did not attend the GFP. High Diploma Scores and First Year GPA were significant predictors of the graduation CGPA of the graduates who did not enroll in the GFP whereas First Year GPA was the main predictors of the CGPA of the graduates who attended the GFP. It was also obvious that the first year GPA showed a higher significant correlation with CGPA among GFP attenders (r = 0.912, p < .01) in comparison to non GFP attenders (r = 0.775, p < .01).Conclusions: This study sheds light into the impact of foundation program on the overall students’ performance in the nursing program. It significantly reveals that GFP, has a positive impact on the overall CGPA, as it equipped the students with the necessary study skills and increased their English proficiency levels.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".