Treatment of depression with antidepressants is primarily a psychological treatment.
Bibliographic record
Abstract
Depression treatment with antidepressants is generally described as evidence-based. However, generalizations to practice recommendations seem to us to rest on the tacit assumption that treatment outcome in research trials is the sum of three factors: specific effects of the drug, expectancy effects (placebo), and spontaneous recovery. Because randomization isolates the specific effects of the drug, trials showing significant drug effects are used as evidence for prescribing the drug regardless of context. Drawing on Wampold's (2001) description of two metamodels of psychotherapy, the authors argue that available empirical evidence indicates that depression treatment with antidepressants is primarily a psychological treatment. This conclusion has far-reaching consequences for the scientific status of contemporary treatments for depression. It also affects what the doctor should focus on in a treatment with antidepressants and how to act when the patient is treatment resistant. In order to achieve the results obtained in clinical trials, the quantity and quality of support from the doctor is more important than pharmacological concerns, such as adequate doses of medicine. When faced with a treatment resistant patient, relationship factors rather than pharmacological factors should be in focus. (PsycINFO Database Record (c) 2010 APA, all rights reserved).
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".