Agricultural wealth better predicts mental wellbeing than market wealth among highly vulnerable households in Haiti: Evidence for the benefits of a multidimensional approach to poverty
Bibliographic record
Abstract
OBJECTIVES: Lack of wealth (poverty) impacts almost every aspect of human biology. Accordingly, many studies include its assessment. In almost all cases, approaches to assessing poverty are based on lack of success within cash economies (eg, lack of income, employment). However, this operationalization deflects attention from alternative forms of poverty that may have the most substantial influence on human wellbeing. We test how a multidimensional measure of poverty that considers agricultural assets expands the explanatory power of the construct of household poverty by associating it with one key aspect of wellbeing: symptoms of mental health. METHODS: We used the case of three highly vulnerable but distinctive communities in Haiti-urban, town with a rural hinterland, and rural. Based on survey responses from adults in 4055 geographically sampled households, linear regression models were used to predict depression and anxiety symptom levels controlling for a wide range of covariates related to detailed measures of material poverty, including cash-economy and agricultural assets, income, financial stress, and food insecurity. RESULTS: Household assets related to the cash economy were significantly associated with lower (ie, better) depression scores (-0.7, [95% CI: -1.2 to, -0.1]) but unrelated to anxiety scores (-0.3 [95% CI: -0.8 to 0.3]). Agricultural wealth was significantly-and more strongly-associated with both reductions in depression symptoms (-1.4 [95% CI: -2.2 to -0.7]) and anxiety symptoms (-1.8 [95% CI: -2.6 to -1.0]). These associations were consistent across the three sites, except in the fully urban site in Port-au-Prince where level of depression symptoms was not significantly associated with household agricultural wealth. CONCLUSIONS: Standard measures of poverty based on success in the cash economy can mask important associations between poverty and wellbeing, in this case related to household-level subsistence capacity and crucial food-producing household assets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".