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Record W4401734708 · doi:10.1097/nna.0000000000001466

Perceived Social Support and Presenteeism Among Nurses

2024· article· en· W4401734708 on OpenAlexaff
Yueling Ma, Xiangeng Zhang, Wanying Ni, Li Zeng, Jialin Wang

Bibliographic record

VenueJONA The Journal of Nursing Administration · 2024
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsPresenteeismSocial supportPsychologyNursingMedicineSocial psychologyAbsenteeism

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to explore the mediating role of psychological capital in the relationship between perceived social support and presenteeism among nurses. BACKGROUND: The concept of presenteeism explored in this study refers to the behavior of nurses who hold on to their jobs despite poor physical or mental health, manifested in poor work productivity and loss of productivity. Perceived social support and psychological capital may help reduce presenteeism. However, there is limited knowledge about the association between perceived social support, psychological capital, and presenteeism among nurses. METHODS: Data were collected through questionnaires from 468 RNs. Data analysis used Pearson's correlation analysis, multiple linear regression, and structural equation model. RESULTS: The results indicated that perceived social support and psychological capital were significantly negatively correlated with nurses' presenteeism. Structural equation modeling revealed that psychological capital mediated the relationship between perceived social support and presenteeism, with a partial mediating effect of -0.191, accounting for 28% of the total effect. CONCLUSIONS: These results identified structural relationships between the 3 variables of perceived social support, psychological capital, and presenteeism and provided a theoretical reference for developing strategies to decrease nurses' presenteeism.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.775
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.049
GPT teacher head0.444
Teacher spread0.395 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations10
Published2024
Admission routes1
Has abstractyes

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