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Record W2614393575 · doi:10.5430/jnep.v7n10p64

The influencing factors of absenteeism among nursing students

2017· article· en· W2614393575 on OpenAlex
Safaa Abdelrahman, Abeer Abdelkader

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Nursing Education and Practice · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAbsenteeismAttendanceEconomic shortageNursingPsychologySample (material)MedicineSocial psychology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.094
GPT teacher head0.520
Teacher spread0.426 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it