Mood Instability as a Precursor to Depressive Illness: Analysis of Data From a Population Survey in Great Britain.
Bibliographic record
Abstract
Levels of mood instability (MI) appear to be high in people with depression, but temporal precedence and possible mechanisms are unknown. We tested hypotheses that: i] MI will be associated with a diagnosis of depression cross-sectionally; ii] MI will predict new onset and maintenance of depression prospectively; iii] the association between MI and depression will be mediated by sleep problems at baseline, new onset alcohol abuse and life events 6 months preceding new onset depression. We used data from the National Psychiatric Morbidity Survey 2000 at baseline (N=8580) and 18 month follow-up (N=2413). Regression modelling controlling for socio-demographic factors, anxiety and hypomanic mood was conducted. Multiple mediational analyses were used to test our conceptual path model. MI was strongly associated with a diagnosis of depression cross-sectionally (OR: 5.28 (95% CI, 3.67-7.59) p< 0.001). MI predicted depression inception (2.43 (1.03 – 5.76) p=0.042) after controlling for important confounders. MI did not predict maintenance of depression. Quality of sleep and severe problems with close friends and family significantly mediated the link between MI and new onset depression (23.05% and 6.19% of the link respectively). Alcohol abuse and divorce were not important mediators. Mood instability is a precursor of a depressive episode but does not worsen the course. Interventions targeting mood instability and sleep problems have the potential to reduce the risk of depressio
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".