What are They Thinking? Examining Complex Links Among Social Withdrawal, Maladaptive Cognitions, and Internalizing Problems in Children and Emerging Adults
Bibliographic record
Abstract
The aim of this doctoral dissertation was to examine a conceptual model linking social withdrawal, maladaptive cognitions, and internalizing difficulties in childhood and emerging adulthood.In Studies 1 and 2, undergraduate students (Study 1: N = 451, Mage = 19.17,SD = 1.40;Study 2: N = 540, Mage = 19.20,SD = 1.49) completed assessments of social withdrawal (shyness, unsociability, social avoidance), maladaptive cognitions (threatening and negative cognitions), and internalizing problems (social anxiety, depressive symptoms).For both samples, results from structural equation modeling suggested partial specificity in the cognitive content associated with internalizing problems.Specifically, whereas threatening cognitions were positively associated with both social anxiety and depressive symptoms, negative cognitions were only positively associated with depressive symptoms.Differences in the social, cognitive, and emotional implications of social withdrawal subtypes also emerged across studies.In Study 1, results suggested that maladaptive cognitions played a mediating role in the links between shyness (but not social avoidance) and internalizing problems.In a conceptual replication of Study 1, results from Study 2 indicated that threatening cognitions mediated the effects of both shyness and social avoidance on social anxiety and depression; however, shyness and social avoidance only displayed indirect effects on depressive symptoms via negative cognitions.The goal of Study 3 was to explore whether a similar model linking withdrawal, peer relations, social cognitions, and internalizing problems could be applied to a sample of early elementary school-aged children (N = 408 children, Mage = 7.10 years, SD = .86).During individual interviews, children completed assessments of their social cognitions (rejection sensitivity, negative Coplan.Rob, I will forever be grateful for your constant support, encouragement, and advice over the years.Thank you for being a confidence booster, an optimist, and (most importantly) a Sens fan.You have taught me so much through your words and actions, and I am so lucky to have you as a mentor and friend.I am also grateful to my committee members, Drs.Deepthi Kamawar, Katie Gunnell, Kasia Muldner, and Larry Nelson for their insightful feedback and kindness.Thank you, Dr. Linda Rose-Krasnor for your advice and support in the development of this dissertation, and Dr. Andrea Howard for your much-needed statistical guidance.Thank you to my fellow Coplan lab mates, past and present, for being there to brainstorm, to collect data, to support, to commiserate, and to play Pictionary.Kristen and Amanda, having you two with me to navigate the crazy journey that is grad school made me feel like I was never going through it alone.To my friends, Steph, Linds
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".