A PROPOSED FRAMEWORK FOR PREVENTING PERFECTIONISM AND PROMOTING RESILIENCE AND MENTAL HEALTH AMONG VULNERABLE CHILDREN AND ADOLESCENTS
Bibliographic record
Abstract
Recent findings suggest that perfectionism is highly prevalent among children and adolescents, and perfectionism can be quite destructive in terms of its links with anxiety, depression, and suicide. In this article, we provide an overview of recent research illustrating the costs and consequences of perfectionism among children and adolescents. We also highlight the heterogeneity that exists among perfectionists and the need for a complex, nuanced approach to assessment and prevention that reflects the achievement and interpersonal concerns of perfectionists. We then summarize past research on the prevention of perfectionism and show that perfectionism is pernicious and resistant to change. Accordingly, interventions must be tailored to address the cognitive and emotion regulation vulnerabilities of perfectionists and their meta‐cognitive beliefs about ability, the self, and the meaning of failure. We conclude by discussing why it is essential to proactively design and implement preventive programs with specific components designed to enhance resilience and reduce levels of risk among perfectionists. We outline several themes that should be incorporated in preventive and intervention efforts designed to address the needs of vulnerable perfectionists.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".