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Record W2903230567 · doi:10.4037/ccn2018616

Critical Care Nurses’ Reasons for Working or Not Working Overtime

2018· article· en· W2903230567 on OpenAlexaffabout
Vanessa M. Lobo, Jenny Ploeg, Anita Fisher, Gladys Peachey, Noori Akhtar‐Danesh

Bibliographic record

VenueCritical Care Nurse · 2018
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOvertimeNoticeNursingFeelingMedicineWork (physics)PsychologySocial psychology

Abstract

fetched live from OpenAlex

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 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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.401
Teacher spread0.343 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2018
Admission routes2
Has abstractyes

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