Revisiting the theoretical and methodological foundations of depression measurement
Bibliographic record
Abstract
Depressive disorders are among the leading causes of global disease burden, but there has been limited progress in understanding the causes of and treatments for these disorders. In this Perspective, we suggest that such progress depends crucially on our ability to measure depression. We review the many problems with depression measurement, including the limited evidence of validity and reliability. These issues raise grave concerns about common uses of depression measures, such as for diagnosis or tracking treatment progress. We argue that shortcomings arise because the measurement of depression rests on shaky methodological and theoretical foundations. Moving forward, we need to break with the field’s tradition, which has, for decades, divorced theories about depression from how we measure it. Instead, we suggest that epistemic iteration, an iterative exchange between theory and measurement, provides a crucial avenue for progressing how we measure depression. Depressive disorders are among the leading causes of global disease burden. In this Perspective, Fried et al. argue that limited progress in understanding, predicting and treating depression despite a wealth of empirical research stems from issues in the methodological and theoretical foundations of depression measurement.
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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 | 0.256 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.015 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".