Stress among Thai farm workers under globalization: A causal model
Bibliographic record
Abstract
This objective study was carried out to discover and model the causal relationships between globalization and stress. The study used a simple random sampling of 600 Thai farm workers. The variables measured were general demographic variables, globalization (i.e. transnational corporations, transnational practices and transnational economics), land holding, the Thai market, Thai state regulation, Thai state social protection and a self-analysed and self-evaluated stress test (SSST). The instrument was modified from the instrument used in past studies. The items were answered using a 4-point Likert-type of scale. However, SSST is a standardised instrument used in Thailand. It was developed by the Department of Mental Health, Ministry of Public Health, Thailand. This instrument was employed to evaluate the respondents’ levels of stress. It was assessed based on 20 items. Its scores were interpreted by stress level and points. It was found to have a Cronbach’s Alpha reliability coefficient of 0.91. Validity was verified by content and construct validity was done by questionnaire. These materials were sent to five professors in order to verify content and construct validity. Reliability was proofed by test-retest reliability. The reliability was 0.94. Data were analysed using the M plus path modelling software i.e. indirect and direct relationships. The results showed direct relationship between stress and globalization i.e. transnational corporations and transnational economics. The modelling revealed that globalization i.e. transnational corporations and transnational economics
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".