Prospective Association Between Perfectionism and Depressive Symptoms in High School Adolescents: Analysis of the Mediating Role of Academic Self‐Efficacy
Bibliographic record
Abstract
ABSTRACT Depression is still not fully understood when it comes to its risk factors. The association with perfectionism is increasingly under scrutiny. Moreover, this link could be elucidated by intermediary variables. This contributes to the fact that the risk factors for depression are not yet fully understood. This study sought to address this gap by examining the role of academic self‐efficacy as a potential mediator of the relationship between perfectionism and depressive symptoms. Mediation analyses were conducted using data collected at three time‐points among 527 high school students (72.9% girls) aged 14–17 years (mean age = 14.9 years, SD = 0.91). Findings suggest that after controlling for baseline levels of depressive symptoms and academic self‐efficacy, there were no prospective associations between perfectionism and depressive symptoms. However, certain findings emerging from this study highlight the role of academic self‐efficacy in perfectionism. These results stress the need for methodological considerations such as controlling for baseline levels of the dependent variables, as well as the need for prevention or early intervention aimed at reducing the onset or continuation of subsequent mental health issues.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".