Cognitive Representations in a Self-regulation Model of Depression: Effects of Self–Other Distinctions, Symptom Severity and Personal Experiences with Depression
Bibliographic record
Abstract
Using Leventhal's self-regulation model, this research investigated cognitive representations of depression in the context of previous work on mental health literacy. Undergraduates rated vignettes that systematically varied the target person (self or other) and depressive symptom severity (mild or moderate). Moderate symptoms, as expected, were viewed as more serious and debilitating than mild symptoms. Also as predicted, a self-positivity bias was evident, with cognitive representations for depression being less extreme for the self, when compared to another. Participants ascribed a shorter timeline, more situational than dispositional causes, less helpfulness for professional assistance, less severe consequences, and lower severity labels for the depressive symptoms that were self-referenced. Many of these self-positivity effects also remained evident in a further vignette that portrayed a month-long escalation of self-referent symptoms from mild to moderate. Greater personal experience with depression also had some limited impact on cognitive representations for the self-referent condition. Overall, these findings provide strong support for several facets of a self-regulation model of depression. They thus indicate a need for depression literacy research to more fully consider the influences of target person and symptom severity on cognitive representations of depression. Practical applications of the results to preventative efforts are also discussed.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".