Depression Symptoms, Mattering, and Anti-mattering: Longitudinal Associations in Young Adulthood
Bibliographic record
Abstract
We examined the dynamic interplay of depression symptoms, mattering (i.e., self-evaluation of importance or significance to others), and anti-mattering across four years of development in young adulthood (age 20–23; N = 452) using a cross-lagged panel model (CLPM). Support for a transactional model between anti-mattering and depression symptoms was found. Specifically, anti-mattering positively predicted later depression symptoms and depression symptoms consistently predicted later anti-mattering. Depression symptoms also shared a negative association with later mattering but not the reverse, supporting a symptoms-driven model of depression symptoms and mattering. Auto-regressive paths, residual covariances, and cross-lagged paths were invariant over time. Accounting for gender, household income, parental education, and fear of COVID-19 as covariates did not change the results. The stability of mattering and anti-mattering suggest careful consideration of how to effectively change these patterns. The implications for assessment and intervention on mattering or anti-mattering in the prevention and treatment of depression are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".