Is Honor Culture Linked With Depression?: Examining the Replicability and Robustness of a Disputed Association at the State and Individual Levels
Bibliographic record
Abstract
A large body of evidence indicates that U.S. honor states exhibit higher suicide rates than do dignity states. Research into one potential precipitating factor, depression, has yielded conflicting evidence, with support for an honor–depression association observed by some researchers but not by others. The present research (a) reassessed this association using more robust measures, (b) extended prior work by also examining suicidal ideation, and (c) examined the associations among honor, depression, and suicidal ideation at both the state and individual levels. Study 1 showed that, after controlling for relevant covariates, state-level honor was associated with higher levels of depression (both major depressive episodes and lifetime depression diagnoses), especially among non-Hispanic White adults. Furthermore, we found the strongest evidence for the honor–depression association using the continuous honor index. We also found that the honor–suicidal ideation association was mediated by depression. Study 2 ( N = 4,235) showed that individual-level honor endorsement was positively associated with depression and suicidal ideation, but not anxiety. Moreover, the individual-level honor–suicidal ideation association was also mediated by depression. We discuss the theoretical and clinical implications of these findings.
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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 | 0.029 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".