Mode of pandemic school instruction associated with distress among military and non-military-connected students
Bibliographic record
Abstract
Introduction: Because of their exposure to unique risk factors, military-connected students may have experienced psychological distress related to mode of school instruction during the COVID-19 pandemic. This study considers psychological distress among military and non-military-connected secondary school students during the pandemic and explores associations between remote/hybrid instruction and distress for both groups. Methods: This study is a secondary analysis of 2020-2021 California Healthy Kids Survey data from 409,152 students in Grades 6 to 12 in California. Results: Military-connected students were significantly more likely to be classified as experiencing moderate (OR = 1.14, 95% CI, 1.07-1.21) or high (OR = 1.23, 95% CI, 1.11-1.36) distress compared to non-military peers. Students receiving in-person instruction were less likely to report moderate (OR = 0.77, 95% CI, 0.84-0.92) or high (OR = 0.82, 95% CI, 0.75-0.91) distress. Among only military-connected students, those receiving in-person instruction were less likely to report moderate or high distress. Discussion: Though all students reported elevated distress related to remote instruction, the adverse consequences of remote instruction may be exacerbated among military-connected students, suggesting the need to direct specific resources to these students. More research is needed to understand mechanisms that may account for distress among students receiving remote instruction and particularly among military-connected students.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".