Feelings of not Mattering and Depressive Symptoms From a Temporal Perspective: A Comparison of the Cross-Lagged Panel Model and Random-Intercept Cross-Lagged Panel Model
Bibliographic record
Abstract
Are feelings of not mattering an antecedent of depressive symptoms, a consequence, or both? Most investigations focus exclusively on feelings of not mattering as an antecedent of depressive symptoms. Our current study examines a vulnerability model, a complication model, and a reciprocal relations model according to a cross-lagged panel model (CLPM) and a random-intercept cross-lagged panel model (RI-CLPM). A sample of 197 community adults completed the General Mattering Scale (GMS), the Anti-Mattering Scale (AMS), and a depression measure at three time points (i.e., baseline, 3 weeks, and 6 weeks). GMS and AMS scores were associated robustly with depressive symptoms at each time point. Other results highlighted the need to distinguish levels of anti-mattering and mattering. CLPM analyses supported a reciprocal relations model of anti-mattering (assessed by the AMS) and depressive symptoms and a complication model linking mattering (assessed by the GMS) and depressive symptoms. The RI-CLPM analyses provided tentative support only for a complication model of anti-mattering and depressive symptoms. Our findings highlight the differences between measures of the mattering construct and the need to adopt a temporal perspective that considers key nuances and the interplay among feelings of mattering, feelings of not mattering, and depression.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".