Anxiety, Depression and Hopelessness in Adolescents: A Structural Equation Model
Bibliographic record
Abstract
Objective: This study tested a structural model, examining the relationship between a latent variable termed demoralization and measured variables (anxiety, depression and hopelessness) in a community sample of Canadian youth. Methods: The combined sample consisted of data collected from four independent studies from 2001 to 2005. Nine hundred and seventy one (n=971) participants were high school students (grades 10-12) from three geographic locations: Calgary, Saskatchewan and Lethbridge. Participants completed the Beck Anxiety Inventory (BAI), Beck Depression Inventory-Revised (BDI-II), Beck Hopelessness Scale (BHS), and demographic survey. Structural equation modeling was used for statistical analysis. Results: The analysis revealed that the final model, including depression, anxiety and hopelessness and one latent variable demor-alization, fit the data (chi-square value, X2 (2) = 7.25, p<.001, goodness of fit indices (CFI=0.99, NFI=0.98) and standard-ized error (0.05). Overall, the findings suggest that close relationships exist among depression, anxiety, hopelessness and demoralization that is stable across demographic variables. Further, the model explains the relationship between sub-clini-cal anxiety, depression and hopelessness. Conclusion: These findings contribute to a theoretical framework, which has impli-cations for educational and clinical intervention. The present findings will help guide further preventative research on exam-ining demoralization as a precursor to sub-clinical anxiety and depression.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".