The Effect of Psychosocial Work Environment on Psychological Strain among Banking Employees in Malaysia
Bibliographic record
Abstract
This study was conducted to examine the psychosocial work environment dimensions as predictors of psychological strain among bank employees specifically bank tellers. The restructuring of financial institutions exposed banking employees to stressful conditions such as unfeasible sales target, lower salaries, high workload and job insecurity. These conditions lead to adverse health outcomes. The researcher integrated the effort-reward imbalance model and the organizational justice model as the psychosocial work environment dimensions that represent the stressful working condition. Thus, these dimensions were hypothesized to affect psychological strain in terms of anxiety, depression and social dysfunction. The data was collected quantitatively by distributing questionnaires to employees in a Malaysian domestic bank. A total number of 306 respondents participated in this study. The data was analysed by performing structural equation modeling using AMOS 22. The finding indicates that effort, reward, overcommitment, procedural justice and interactional justice significantly affect psychological strain. Only effort-reward ratio was not significant in predicting psychological strain. These findings added the empirical evidence in the stress-strain literature that involves psychosocial work environment specifically among banking employees in Malaysian context.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".