Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To examine the implications of the association between personality and depression for the understanding, assessment, and treatment of major depression. METHOD: A broad range of peer-reviewed manuscripts relevant to personality and depression was reviewed. Particular emphasis was placed on etiology, stability, diagnosis, and treatment implications. RESULTS: Personality features in depressed samples reliably differ from those of healthy samples. The associations between personality and depression are consistent with a variety of causal models; these models can best be compared through longitudinal research. Research demonstrates that attention to personality features can be useful in diagnosis and treatment. Indeed, personality information has been on the forefront of recent efforts to advance the current diagnostic classification system. Moreover, personality dimensions have shown recent promise in the prediction of differential treatment outcome. For example, neuroticism is associated with preferential response to pharmacotherapy rather than psychotherapy. CONCLUSIONS: Consideration of personality features is crucial to the understanding and management of major depression.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it