Hard Times and Good Friends: Negative Life Events and Social Support in Patients with Seasonal and Nonseasonal Depression
Bibliographic record
Abstract
OBJECTIVE: Although a relatively large body of research has now accumulated concerning the relation between negative life events, social support, and major depressive disorder (MDD), little is known about the relation between seasonal affective disorder and these psychosocial variables. This study aimed to compare baseline levels of negative life events (NLEs) and perceived social support (SS) in patients with seasonal and nonseasonal depression. METHOD: Canadian patients with winter seasonal affective disorder (SAD) (n = 26) and nonseasonal recurrent MDD (n = 66) completed measures of recent NLEs (the List of Threatening Experiences) and perceived SS (the Social Support Survey) prior to treatment. RESULTS: No significant between-group differences were observed in mean number of NLEs experienced or in quality of SS. Perceived SS was impaired in both groups, compared with patients with chronic medical conditions. CONCLUSIONS: The results of this study complement those of previous research reporting increased incidence of NLEs and decreased SS in primary care patients with high seasonality in the UK. Future research is required to determine the causal relation between these psychosocial risk factors and SAD and to assess whether they have an effect on, or are affected by, treatment interventions for SAD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".