Reasons for presenteeism in nurses working in geriatric settings: A qualitative study
Bibliographic record
Abstract
Objective: To explore the perceived causes of presenteeism in nurses on geriatric wards.Background: Presenteeism, defined as working when unwell, is associated with lost productivity and increased absenteeism. It is more commonly reported by employees in the healthcare sector than other sectors.Methods: An exploratory, qualitative study using semi-structured interviews, thematically analysed. Data collected via 18 recorded interviews with nurses working with patients on geriatric medical wards in Malta.Results: Four major themes emerged that related to nurses’ decisions to engage in presenteeism: illness perceptions, which included participants’ views and experiences of their own health complaints; attitudes to their employing organisation, co-workers and patients; organisational aspects such as culture and administrative arrangements; and personal reasons including illness behaviour preference and personal circumstances.Conclusions: Nurses’ decisions to attend work when unwell were reported as dependent upon four themes. Further studies are warranted to determine if findings are applicable to nurse populations other than those represented in this study.Implications: Workplace health promotion initiatives should target nurses’ management of their own health, particularly if they have chronic illnesses. Workplace policies and arrangements should be examined with a view to controlling presenteeism.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".