Healthy and unhealthy dependence: Implications for major depression
Bibliographic record
Abstract
OBJECTIVES: To examine the contribution of varying levels of dependency to Axis I and Axis II disorders, and to the recurrence of major depression in a graduate student sample diagnosed with a history of the disorder. METHODS: At Time 1, participants were interviewed to confirm a current or past episode of major depression along with the presence of Axis II and other current or past Axis I disorders. Various measures of dependency were administered including the Depressive Experiences Questionnaire (DEQ; Blatt, D'Afflitti, & Quinlan, 1976), the 3-Vector Dependency Inventory (3VDI; Pincus & Gurtman, 1995), and the Personal Style Inventory (PSI; Robins et al., 1994). Participants were interviewed 20 months later to determine the recurrence of a depressive episode. RESULTS: A factor analysis conducted on scale scores for each dependency measure resulted in three factors labelled 'unhealthy', 'intermediate', and 'healthy' dependence. Controlling for history of major depression, structural equation modelling found 'unhealthy' dependence to be the only predictor of recurrences of major depression and Axis II disorders, while 'healthy' dependence was related to fewer depressive symptoms. CONCLUSIONS: These results have important implications for the conceptualization of the dependency construct.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".