The influencing factors of absenteeism among nursing students
Bibliographic record
Abstract
The absence of nursing students from classrooms and clinical has a negative impact on their performance and prolongs the length of their studying. The aim of this study is to identify the influencing factors of absenteeism among nursing students at Minia University. This study was conducted at the Faculty of Nursing at Minia University, and Minia University Hospitals. The sample of students that participated in the study represented all academic levels as follows: first level 49/370, second level 49/292, third level 52/248, and fourth level 50/220. Data were collected with the use of a self-administered questionnaire. This study revealed that influencing factors of absenteeism among the studied nursing students indicated that the highest mean scores were associated with teaching factors, followed by assessment factor where means scores were (18.3 ± 4.5, and 17.1 ± 5.6, respectively). Also, the lowest mean score reported was associated with social problems (mean = 8.9 ± 3.2). This study concluded that the most common contributory factors in student absenteeism were related to teaching factors including a shortage of staff in the clinical area, and lack of understanding of the lecture content. Recommendations: Providing a safe learning environment, keeping accurate records of attendance and calculating absenteeism rates at frequent intervals are required for identifying each individual’s pattern of attendance.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".