Associations between Depressive Symptoms and Social Support in Adults with Diabetes: Comparing Directionality Hypotheses with a Longitudinal Cohort
Bibliographic record
Abstract
BACKGROUND: Individuals with diabetes are at increased risk of elevated depressive symptoms, and social support has been identified as a key factor in the health of this population. Cross-sectional associations between depressive symptoms and social support have been demonstrated. Three classes of hypotheses differentially describe the direction of this association: (1) depressive symptoms influence social support, (2) social support influences depressive symptoms, and (3) reciprocal associations exist between depressive symptoms and social support. PURPOSE: The aim of this study was to compare these hypotheses. METHODS: Depressive symptoms and social support were measured via telephone survey in a large cohort study of individuals with diabetes (n = 1754) in Quebec, Canada. After baseline, data were collected annually for 4 years. Path models depicting each hypothesis, as well as a stability model containing only autoregressive effects, were generated, and model fit was compared with Akaike's Information Criterion (AIC). RESULTS: The reciprocal model was selected as the best fitting model because it had the lowest AIC. This model demonstrated that depressive symptoms predicted subsequent social support at all time points and that social support predicted subsequent depressive symptoms at most time points. CONCLUSIONS: It appears that the association between depressive symptoms and social support in people with diabetes is best characterized as reciprocal. Results underscore the importance of directly comparing competing hypotheses and offer a more accurate depiction of the association between depressive symptoms and social support among people with diabetes.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".