Cross-generational effects of discrimination among immigrant mothers: Perceived discrimination predicts child's healthcare visits for illness.
Bibliographic record
Abstract
OBJECTIVE: This study tested whether an immigrant mother's perception of ethnic and language-based discrimination affects the health of her child (indexed by the child's frequency of sick visits to the doctor, adjusting for well-visits), as a function of her ethnic-group attachment and length of U.S. residency. METHOD: A community-based sample of 98 immigrant Dominican and Mexican mothers of normally developing 14-month-old children were interviewed. Mothers reported their perceived ethnic and language-based discrimination, degree of ethnic-group attachment, length of time in the United States, and frequency of their child's doctor visits for both illness and routine (healthy) exams. RESULTS: Among more recent immigrants, greater perceived ethnic and language-based discrimination were associated with more frequent sick-child visits, but only among those reporting low ethnic-group attachment. The associations between both forms of perceived discrimination and sick-child visits were not observed among mothers reporting high ethnic-group attachment. Among more established immigrants, perceived language-based discrimination was associated with more frequent sick-child visits regardless of ethnic-group attachment. CONCLUSION: These results suggest that a Latina mother's experience with ethnic and language-based discrimination is associated with her child's health, as indicated by doctor visits for illness, but that strong ethnic-group attachment may mitigate this association among recent immigrants.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".