Perceived microaggressions and quality of life: the mediating role of personal resources and social support among people with African migration background in Germany
Bibliographic record
Abstract
In contemporary discourse, microaggressions are not mere fleeting occurrences but pervasive daily experiences that significantly influence individual and collective well-being. This current study delves into the role of personal resources and social support as mediators in the relationship between microaggressions and quality of life. The study analyses cross-sectional data from 604 African migrants in Germany, employing Structural Equation Modelling techniques. Five direct associations were examined alongside three separate mediation analyses to evaluate the predictive effect of microaggressions on quality of life through personal resources, social support, and the combined influence of both. The results indicate a negative association between microaggressions, personal resources, social support, and quality of life. Microaggressions constrain personal resources and social support, thereby compromising quality of life, as evidenced by the attenuating effects observed in the mediation analyses. Furthermore, the serial mediation model highlights the distinct contributions of personal resources and social capital. The findings underscore the serialised nature of microaggression’s impact on quality of life, suggesting that neither personal resources nor social support can fully mitigate its effects. This study posits that microaggressions manifest through migrants’ social interactions and exchanges, undermining personal resources and social support networks essential for enhancing their quality of life.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".