Mattering and Anti-Mattering in Emotion Regulation and Life Satisfaction: A Mediational Analysis of Stress and Distress During the COVID-19 Pandemic
Bibliographic record
Abstract
The current study focused primarily on the associations that feelings of not mattering have with life satisfaction, stress, and distress among students trying to cope with the uncertain and novel circumstances brought about by the COVID-19 pandemic. A sample of 350 University students from Italy completed measures that included the General Mattering Scale and the Anti-Mattering Scale, as well as measures of self-esteem, difficulties in emotion regulation, life satisfaction, perceived stress, anxiety, and depression. Psychometric analyses confirmed the factor structure, reliability, and validity of the General Mattering Scale and the Anti-Mattering Scale. As expected, feelings of not mattering were associated with lower life satisfaction as well as with greater reported difficulties in emotion regulation, stress, and distress. Mattering and self-esteem were both unique predictors of levels of life satisfaction during the pandemic. The results of mediational analyses suggested that individuals who feel as though they do not matter may be especially vulnerable to stress, depression, and anxiety and this may promote a decline in life satisfaction. Given the potential destructiveness of feelings of not mattering, in general but especially during a global pandemic, it is essential to proactively develop interventions and programs that are designed to enhance feelings of mattering and reduce anti-mattering experiences and feelings.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".