Connections between academic motivation and benefits to low‐grade inflammatory regulation among the socioeconomically advantaged
Bibliographic record
Abstract
INTRODUCTION: Working to reach school goals during adolescence and rise in the socioeconomic hierarchy can have unexpected negative consequences for physical health, which are often linked to inflammation. However, certain forms of academic motivation, like finding meaning in difficulty, can benefit health and well-being. The current study tests whether socioeconomic resources explain this paradox and moderate the relationship between motivational processes and indicators of inflammation among adolescents. Having greater socioeconomic resources may provide the circumstances necessary to experience a beneficial connection between higher school motivation and lower indicators of inflammation. METHOD: = 14). The survey included a key measure of motivation indicating how students respond to experiences of academic difficulty. The health screening produced assays of C-reactive protein and interleukin 6 from antecubital blood samples, which provided an indicator of low-grade inflammation. RESULTS: Multiple linear regression analyses demonstrated the expected pattern of moderation, such that students with high (but not low) socioeconomic resources experienced a positive connection between motivation and indicators of inflammatory regulation, especially C-reactive protein. CONCLUSIONS: The findings provide an important contribution to understanding the complex links between achievement and health. Future research on the health costs of mobility should consider the health benefits of motivation that may be observed uniquely among the socioeconomically advantaged. Further, education institutions should promote motivation in ways that are connected to health sustaining forms of support for all 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 | 0.001 | 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 | 0.000 | 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) | 0.003 | 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".