Depression Treatment Preferences of Hispanic Individuals: Exploring the Influence of Ethnicity, Language, and Explanatory Models
Bibliographic record
Abstract
PURPOSE: there is uncertainty regarding Hispanic individuals' depression treatment preferences, particularly regarding antidepressant medication, the most available primary care option. We assessed whether this uncertainty reflected heterogeneity among subgroups of Hispanic persons and investigated possible mechanisms. Specifically, we examined factors associated with medication preferences in non-Hispanic white and Spanish-speaking and English-speaking Hispanic persons. METHODS: we analyzed data from a follow-up telephone interview of 839 non-Hispanic white and 139 Hispanic respondents originally surveyed via the 2008 California Behavioral Risk Factor Surveillance System. Measures included treatment preferences (for treatment plans including vs not including antidepressants); depression history and current symptoms; sociodemographics; and psychological measures. RESULTS: compared with non-Hispanic white respondents (adjusting for age, sex, history of depression diagnosis, and current depression symptoms), Spanish-speaking Hispanic (adjusted odds ratio [AOR] 0.41; 95% CI, 0.19-0.90) but not English-speaking Hispanic (AOR, 1.18; 95% CI, 0.60-2.33) respondents had a lower preference for antidepressant inclusive options. Endorsing a biomedical explanation of depression was associated with a preference for antidepressant inclusive options (AOR, 4.76; 95% CI, 3.13-7.14) for all respondents and accounted for the effect of Spanish-language interview. Accounting for other factors did not change these relationships, although older age and history of depression diagnosis remained significant predictors of antidepressant inclusive treatment preference for all respondents. CONCLUSIONS: Spanish-language interview and less belief in a biomedical explanation for depression were associated with Hispanic respondents' lower preferences for pharmacologic treatment of depression; ethnicity was not. Understanding treatment preferences and illness beliefs could help optimize depression treatment in primary care.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".