Kesehatan Mental dalam Dunia Kerja : Tantangan dan Tanggung Jawab Negara Menuju Indonesia Emas 2045
Bibliographic record
Abstract
Mental health has emerged as a strategic issue in global employment, particularly amid rising work demands and post-pandemic social transformation. In Indonesia, the existing labor regulatory framework does not explicitly address psychosocial risks in the workplace, despite growing psychological burdens among young workers, especially Generation Z. This article examines the integration of mental health into national labor policy through a multidisciplinary lens. It analyzes historical developments, theoretical frameworks such as the Job Demands- Resources Model and Psychosocial Safety Climate, and policy practices from countries including Japan, the United Kingdom, Canada, and Australia. Based on empirical and comparative insights, the article proposes four strategic pillars: regulatory harmonization, social security reform, primary care capacity-building, and multi-stakeholder collaboration with public literacy initiatives. The conclusion emphasizes the critical need to embed mental health in employment systems as a strategic prerequisite to realizing Indonesia’s Vision 2045 and enhancing the competitiveness of its workforce.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".