Suffering, psychological distress, and well‐being in Indonesia: A prospective cohort study
Bibliographic record
Abstract
Research on the subjective experience of suffering has typically focussed on older clinical samples living in Western, educated, industrialised, rich, and democratic (WEIRD) countries. To further extend the existing body of empirical research on suffering to less WEIRD contexts, we use three waves of data (Wave 1: December 2020; Wave 2: January 2021; Wave 3: February 2021) from a sample of nonclinical Indonesian adults (n = 594) to examine associations between suffering, two indices of psychological distress, and 10 facets of well-being. In our primary analysis, we estimated a series of multiple regression models that adjusted for a range of sociodemographic characteristics, financial and material stability, religious/spiritual factors, prior values of overall suffering, and prior values of each outcome assessed in Wave 1. Results indicated that overall suffering assessed in Wave 2 was associated with an increase in both indices of psychological distress and a decrease in eight facets of well-being assessed in Wave 3. Using a similar analytic approach, results from a secondary analysis indicated that higher scores on both indices of psychological distress and lower scores on seven of the well-being facets assessed in Wave 2 were associated with worse subsequent overall suffering assessed in Wave 3. These findings contribute to empirical literature on the implications of suffering for well-being.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".