Measuring Latinx/@ immigrant experiences and mental health: Adaptation of discrimination and historical loss scales.
Bibliographic record
Abstract
= 52) were recruited through community partner organizations and completed four qualitative and quantitative interviews over a 12-month period. The present analysis draws on the baseline quantitative data. Results show it is possible to adapt standardized measures of discrimination developed to assess the experiences of other racial/ethnic groups; however, the most common responses involved response options added by our research team. For the historical loss instrument, there was a high frequency of "never" answers for many items, suggesting that they were not relevant for participants or did not capture their experiences of loss. As with the discrimination measures, the items we added resonated the most with participants. The contexts of discrimination and loss for Latinx/@ immigrant populations are complex, thus the tools we use to measure these experiences and their impact on health must account for this complexity. This study contributes to these endeavors through involving community members in the conceptualization and measurement of discrimination and historical loss among Latinx/@ immigrants. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".