Assessing the longitudinal course of depression and economic integration of south‐east Asian refugees: an application of latent growth curve analysis
Bibliographic record
Abstract
This paper has both methodological and substantive application for mental-health researchers. Methodologically, it presents the latent growth curve (LGC) technique within a structural equation modelling (SEM) framework as a powerful tool to analyse change in depressive symptoms and potential correlates of such changes. The rationale for LGC analysis and subsequent elaboration of this statistical approach are presented. The limitations of traditional analytical methods are also addressed. Substantively, the paper considers socio-contextual factors as correlates of change in symptoms, and examines the dynamic systematic relationship with the degree of economic integration of south-east Asian immigrants in Canada over time. Using the LGC technique, this study also investigated how the longitudinal course of subclinical depression places individuals at risk for developing full-blown major depression. The LGC results provided strong evidence for the reciprocal influence between economic integration and subclinical depression of immigrants. The initial level of economic integration negatively influenced the rate of change in subclinical depression whereas the initial level of subclinical depression negatively influenced the rate of change in economic integration. Both initial level and the rate of change in subclinical depression placed individuals at risk for full-blown major depression. However, traditional auto-regressive models were not capable of revealing these dynamic associations. Thus, an investigation of within-individual change in symptoms and potential correlates of such changes is necessary to understand the process that results in full-blown mental disorder.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".