Exploring Potential Predictors of Psychological Distress among Employees: A Systematic Review
Bibliographic record
Abstract
Psychological distress is becoming more prominent among employees in various workplaces.Previous studies have reported that some forms of psychological distress such as stress, depression and anxiety, have been suffered by a significant proportion of employees globally.These conditions could lead to harmful consequences, affecting the physical, social and work functioning of the employee, if not addressed at an earlier stage.This study aims to identify the predictors of psychological distress being suffered by employees in respective of their profession.To achieve this aim, a systematic review of related literature was conducted.Various databases including Scopus, PsychINFO, MEDLINE and Google Scholar, were searched for related studies published from 2009 to 2019.Out of the 1219 studies found from the literature search, only 79 studies met the inclusion criteria to be included in this research.A total of 22 factors were collated from the studies reviewed, as potential predictors of psychological distress, which includes lack of exercise, poor time management skills, high workload and poor working relationship.These factors were further grouped into five constructs using thematic analysis, namely lifestyle choices, physiological health, job attitudes, work factors and psychosocial factors.This study, therefore, contributes to the literature on occupational psychology.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".