Structural Equation Modeling and Relationships Between Mental Wellbeing, Resilience and Self-stigma
Bibliographic record
Abstract
This study investigated mental wellbeing of postgraduate and undergraduate students of International Islamic University Malaysia. Precisely, the objective of this study was to verify the validity of a proposed mental wellbeing model that include resilience and self-stigma exogenous variables, another aim of the survey was to determine if gender, education level, material status, international and non-international student and location where they live moderated the associations between mental wellbeing and its predictors. The survey adapted existing instruments: Warwick-Edinburgh Mental Well-being (WEMWB) Scale of 14-item questionnaire, Brief Resilience Scale of 6-item questionnaire and Self-Stigma of Seeking Help Scale (SSOSH) 10-item questionnaire and a demographic survey was developed for this study. The data were collected randomly from 315 student of International Islamic University Malaysia. To address the research objectives, the data were analyzed with confirmatory factor analysis and structural equation modeling. The results obtained by this study shows that, the model had an excellent fit, because RMSEA .007 < .06, CFI .999 > .95, Based on these results it can be concluded that the proposed model is valid, approving the very first hypothesis for this study. Additionally, gender, educational, location when they live and International and Malaysian student, did not moderate the predictor-mental wellbeing relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".