The Impact of Self‐ and Partner Schemas on Information Processing and Treatment Seeking in Depression
Bibliographic record
Abstract
Globally, depression affects millions of individuals (and their loved ones) each year. Given its pervasiveness, multifaceted approaches are needed to improve our understanding of depression and how its underlying vulnerability factors may shape perceptions of mental health communication. This chapter adopts a cognitive perspective of depression, beginning with an overview of Beck's cognitive theory and the role of schemas in information processing. We discuss the extant literature on the origins of negative self-schemas in depression and how they may shape maladaptive thoughts and cognitions. Additionally, we discuss more recent research on partner schemas and their impact on personal and relational well-being. In this section, we highlight the Dyadic Partner Schema Model as a theoretical approach to understanding the links between beliefs about one's partner, relationship distress, and depressive symptoms. We then turn to applications of schemas and their impact on treatment, including cognitive-behavior therapy for depression, as well as help-seeking behaviors. Finally, we consider key challenges and biases in mental health communication for researchers and healthcare providers, emphasizing the importance of tailoring communication strategies to individuals’ belief systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".