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Record W2964051496 · doi:10.5430/ijhe.v8n4p153

The Influence of Resilience on Psychological Well-Being of Malaysian University Undergraduates

2019· article· en· W2964051496 on OpenAlexvenueno aff
Izazol Idris, Ahmad Zamri Khairani, Hasni Shamsuddin

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsPsychologyPsychological resilienceResilience (materials science)Psychological healthPsychological well-beingVariance (accounting)Applied psychologySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Psychological well-being is fundamental to the overall health of undergraduates, particularly to enable them to address challenges at the university. A review of related literature showed that there are various factors influencing individual’s psychological well-being. The purpose of this study is to investigate the influence of resilience on the psychological well-being of university undergraduates. For this purpose, a total of 200 undergraduates from local public universities (male = 90, female = 110) participated in this exploratory study. Responses were analysed using Smart PLS 3.0 to model the influence of the two variables. Results demonstrated two significant findings. Firstly, reliable and valid adapted instruments measuring resilience and psychological well-being were established, and secondly, resilience is a significant predictor and it explained 48.2% variance in psychological well-being. The findings are discussed in relation to the development of a model that relates the two constructs.

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)0.000
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 score0.579
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.334
Teacher spread0.323 · 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

Citations31
Published2019
Admission routes1
Has abstractyes

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