An Intersectional Mixed Methods Approach to Understand American Indian Men’s Health
Bibliographic record
Abstract
This study used a parallel convergent mixed methods design with TribalCrit theory and intersectionality as analytical frameworks to identify how American Indian men's identities intersect with broader structures and systems to shape their eating and physical activity choices and behaviors, and to elicit recommendations for a men's lifestyle intervention. AI men were recruited in Minneapolis, Minnesota and Portland, Oregon between March and December 2017 and in Phoenix, Arizona in December 2019 to participate in a survey and focus groups. The survey included demographic questions and questions about physical and cultural activities men engage in, perceived social support for lifestyle behaviors, masculine characteristics, and values important to American Indian men. The 6-item Kessler Psychological Distress Scale was used to assess psychological distress. Focus groups were audio recorded and transcribed for a phenomenological analysis. Descriptive statistics and correlations were computed for survey data. We conducted 15 focus groups with 151 adult American Indian men in three urban sites. The mean age of participants ranged from 36-51 across the sites; 7%-32% were college graduates; 13%-22% were currently married and 28%-41% were working full time. The most important values reported by participants were being: strong mentally and emotionally, a good parent, responsible, spiritual, and a good spouse or partner. On the K6 psychological distress scale, 63%-70% scored ≥5 but <13 (moderate mental distress), and 8%-15% scored ≥13 indicating severe mental distress. Younger age was significantly correlated with higher mean K6 score (p < .0001). Colonizers and missionaries that settled in the U.S. imposed cultural and gender hegemony which enforced a patriarchal capitalist system that have had long-lasting and deleterious effects on American Indians, particularly American Indian men.
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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 | 0.055 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".