Critical Care Nurses’ Reasons for Working or Not Working Overtime
Bibliographic record
Abstract
BACKGROUND: Around the world, registered nurses are working increasing amounts of overtime. This is particularly true in critical care environments, which experience unpredictable fluctuations in patient volume and acuity, combined with a need for more specialized nurses. OBJECTIVE: To explore critical care nurses' reasons for working or not working overtime. METHODS: A semistructured interview guide was used to interview 28 frontline nurses from 11 critical care units in Ontario, Canada. Analysis was guided by Thorne's interpretive description methodology. RESULTS: Participants' reasons for working overtime included (1) financial gain (96% of participants); (2) helping and being with colleagues (68%); (3) continuity for nurses and patients (39%); and (4) accelerated career development (39%). Their reasons for not working overtime were (1) feeling tired and tired of being at work (50%); (2) having established plans (71%); and (3) not receiving enough notice (61%). CONCLUSIONS: Findings from this study provide important variations and extension of existing literature on the topic, and appear to be the first reported in Canadian critical care units. Additional research is required to understand administrative decision-making processes that lead to the use of overtime.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".