The impact of violence exposure and posttraumatic stress on the academic functioning of Latine middle school students: The moderating role of language use.
Bibliographic record
Abstract
Latine youth are at an increased risk of exposure to stress and trauma, and face significant educational inequities and cultural stressors. Thus, the current cross-sectional survey examined the role of acculturation based on language use as a moderator in the relationship between traumatic stress and academic functioning among immigrant and U.S. born Latine youth in one community middle school (N = 130). We examined language use as a moderator in the relationship between violence exposure and academic outcomes, as well as between PTSD symptoms and academic outcomes. Analyses revealed that students who reported higher levels of Spanish language use, and higher levels of violence exposure, had significantly lower GPAs. Additionally, we found that students who spoke less Spanish, and who had higher PTSD symptoms, had a lower GPA. Present findings highlight the importance of and link between mental health, cultural-linguistic factors, and academic performance in Latine youth. Particularly, self-reported Spanish language use appears to be both a buffering and risk factor as related to the academic achievement of Latine middle school students in the U.S. However, there is a need to further explore these pathways and linkages, particularly for Latine youth, who are disadvantaged by higher exposure to stressors and by multiple inequities in education. By investing in policies and practices that affirm Latine students' cultural and linguistic strengths while addressing their unique challenges, we can foster environments where they thrive academically, emotionally, and socially.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".