Social Network Impacts and Moderators of Depression Among Indigenous Maya People Remaining in Place of Origin in the Migrant-Sending Guatemalan Western Highlands
Bibliographic record
Abstract
Remaining in the place of origin while family, friends, and neighbors emigrate can have adverse effects on psychological well-being. Specific important relationships absent from one’s social network can be especially impactful, while other relationships and network characteristics still available in the home network can be protective against psychological distress. The highlands of western Guatemala experience emigration at high rates and changing social network structures, affecting the mental health of those remaining at home. This study uses socio-centric network data from a single community (N = 653) to investigate the association between having emigrant ties in the United States and experiencing depressive symptoms according to an adapted CESD-20 scale. We also explore which types of relationships and network characteristics increase the likelihood of reporting depressive symptoms or moderate the relationship between emigration and depression. Our results indicate that having emigrant ties and more of them increases the odds of depression, even if only one friend or neighbor emigrated. Those with lower levels of education were also more likely to report depressive symptoms. However, more connected networks offered some protection from depression. Certain critical relationships still available at home, like a mother or sibling, lowered the likelihood of depression. For women, higher transitivity, or network cohesiveness, moderated the relationship between emigration and depression, and for men, a higher proportion of their connections outside of the household than within the household moderated that relationship. These findings may offer some insight into important relationships and network structures that may be leveraged to ease the mental health burden for those remaining at home while friends and loved ones emigrate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".