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Record W2793348911 · doi:10.5539/ass.v14n4p18

The Role of Hope and Self-efficacy on Nurses’ Subjective Well-being

2018· article· en· W2793348911 on OpenAlexvenueno aff
Rui-Ming Liu, Pan Zeng, Peng Quan

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-efficacyPsychologyMediationSubjective well-beingScale (ratio)Agency (philosophy)Well-beingSocial psychologyClinical psychologyPsychotherapistSociology

Abstract

fetched live from OpenAlex

Although subjective well-being is considered important for nurses, the relationship between hope, self-efficacy, and subjective well-being among nurses has rarely been assessed. This study purposes to explore the relationships between hope, self-efficacy, and subjective well-being. The analysis relies on data from 1757 female nurses in 3 hospitals in China. Nurses completed a demographic form, General Self-efficacy Scale, Hope Scale, General Well-Being Schedule. A mediate model of the hypothesized relationships between the constructs was tested. Significant direct relationships of hope, self-efficacy, and subjective well-being were displayed. Mediation analyses reveal that the impact of self-efficacy on subjective well-being is partially mediated by two components of hope, agency and pathways. Hope was shown to be a key mediator for the relationships between self-efficacy and subjective well-being. These findings advance current understandings on the hopeful thinking in nurses.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.006
GPT teacher head0.294
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations17
Published2018
Admission routes1
Has abstractyes

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