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Record W3033563806 · doi:10.5430/ijhe.v9n4p138

Investigate the Relation between Psychological Well-being, Self-efficacy and Positive Thinking at Prince Sattam bin Abdul Aziz University Students

2020· article· en· W3033563806 on OpenAlexvenueno aff
Maha Ahmed Hussein Alkhatib

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
FundersDeanship of Scientific Research, Prince Sattam bin Abdulaziz UniversityPrince Sattam bin Abdulaziz University
KeywordsPsychologySelf-efficacyPositive relationshipPsychological well-beingScale (ratio)Positive correlationClinical psychologyCritical thinkingSocial psychologyMedicineMathematics educationInternal medicine

Abstract

fetched live from OpenAlex

The current study aimed to investigate the relationship between psychological well-being, self-efficacy and positive thinking, among Prince Sattam Bin Abdul Aziz University’s students in Saudi Arabia. To answer the study questions, three questionnaires were administrated, two were submitted by the researcher (psychological well-being and self-efficacy), positive thinking scale by (Radi & Metib, 2017) to 350 university students with range age of 18 to 36 years old. The study adopted a descriptive design to measure the degree of correlation between variables, Results of the study showed that students have moderate psychological well-being level, and that there was a positive relationship between psychological well-being; self-efficacy and positive thinking, also research results indicated that there was a positive relationship between self-efficacy and positive thinking, but the results showed that (gender, faculty, acedamic level) had no impact on psychological well-being or positive thinking. The impact was within (academic level) on self-efficacy in benefit of master degree group.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.416
Teacher spread0.374 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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