Bibliographic record
Abstract
This study explores absenteeism patterns and trends among a group of third-year student nurses. A questionnaire was used to elicit information about absence behaviour from 110 students at two hospital sites. Retrospective analysis of attendance records of 70 of these students, covering a period of 123 weeks, was also performed to determine absenteeism trends. The findings of the study reveal that 1567 days were lost because of absenteeism during this period on 1027 episodes. This represents a time lost index, which is the amount of days lost expressed as a percentage of total days available, of 4% among the group. Most absenteeism episodes lasted 3 days or less, with 73% of episodes lasting only 1 day. Absenteeism commencing either on Mondays or Fridays accounted for more than half of the absenteeism episodes in the group. Voluntary absence was a reported feature of this group, which occurred more frequently from lectures than wards. The main reasons cited for absence from both lectures and ward duties were personal and social commitments and stress. Students' views on nursing as a career and responses to factors that may cause stress were examined and revealed an association with reported absence behaviour.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".