The AFM Experience Among First Nations, Indigenous Populations, and Ethnic Minorities
Bibliographic record
Abstract
Abstract Although there is no single definition of an ethnic minority, there is some consensus that members of an ethnic minority share a sense of belonging and linguistic and cultural backgrounds. Many minorities suffer marginalization and struggle to survive in precarious conditions that the presence of substance use problems might aggravate. In this chapter, we aim to analyze how living with a relative who consumes alcohol or other substances impacts the health of families that belong to an ethnic minority, specifically, an Indigenous community in central Mexico. The information analyzed was obtained through group interviews with members of an Otomí community in central Mexico; the majority were women aged 17–65. Five topics were addressed in the interviews: (a) How does drinking occur in the community? (b) Problems related to consumption, (c) How does it affect family members? (d) What should be done when someone drinks a lot? (e) What can be done to prevent the problem? The participants agreed that consumption patterns in the community have changed due to migration and that there are gendered social norms concerning drinking. Excessive alcohol use alters the routine and quality time spent with the family. It affects the mental health of other members, especially women, who must carry the “burden” alone and must take care of the rest of the family, including the relative who drinks. Alcohol use is accepted as part of the life of the people who live in the area, and it can become a severe problem that has equally significant consequences for the health of the person who drinks and the health of the family and the household economy, as well as having social repercussions. The views expressed by the participants must be considered to develop culturally appropriate programs to address this health problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".