Recurrence in major depression: A conceptual analysis.
Bibliographic record
Abstract
Theory and research on major depression have increasingly assumed a recurrent and chronic disease model. Yet not all people who become depressed suffer recurrences, suggesting that depression is also an acute, time-limited condition. However, few if any risk indicators are available to forecast which of the initially depressed will or will not recur. This prognostic impasse may be a result of problems in conceptualizing the nature of recurrence in depression. In the current paper we first provide a conceptual analysis of the assumptions and theoretical systems that presently structure thinking on recurrence. This analysis reveals key concerns that have distorted views about the long-term course of depression. Second, as a consequence of these theoretical problems we suggest that investigative attention has been biased toward recurrent forms of depression and away from acute, time-limited conditions. Third, an analysis of how these theoretical problems have influenced research practices reveals that an essential comparison group has been omitted from research on recurrence: people with a single lifetime episode of depression. We suggest that this startling omission may explain why so few predictors of recurrence have as yet been found. Finally, we examine the reasons for this oversight, document the validity of depression as an acute, time-limited disorder, and provide suggestions for future research with the goal of discovering early risk indicators for recurrent 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".