Bibliographic record
Abstract
Over the past few decades, theory and research on depression have increasingly focused on the recurrent and chronic nature of the disorder. These recurrent and chronic forms of depression are extremely important to study, as they may account for the bulk of the burden associated with the disorder. Paradoxically, however, research focusing on depression as a recurrent condition has generally failed to reveal any useful early indicators of risk for recurrence. We suggest that this present impasse is due to the lack of recognition that depression can also be an acute, time-limited condition. We argue that individuals with acute, single lifetime episodes of depression have been systematically eclipsed from the research agenda, thereby effectively preventing the discovery of factors that may predict who, after experiencing a first lifetime episode of depression, goes on to have a recurrent or chronic clinical course. Greater awareness of the high prevalence of people with a single lifetime episode of depression, and the development of research designs that identify these individuals and allow comparisons with those who have recurrent forms of the disorder, could yield substantial gains in understanding the lifetime pathology of this devastating mental illness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".