Combining pharmacotherapy and psychotherapy - the example of depressive disorders : review article
Bibliographic record
Abstract
The debate about whether to use psychopharmacology psychotherapy has shifted from an either / or debate to a rational discussion of combination therapy sequential therapy. This paper discusses the reasons for this academic shift. The implications of this scientific debate are the choice of modality in a particular clinical condition, augmentation effects, prevention of relapse and recurrence with continuation treatment, the sequential application of psychotherapy and pharmacotherapy, improvement of compliance. Major depression is used as an example of a disorder where the combination of psychotherapy and pharmacotherapy offer great advantages. The evidence for the efficacy of this combination reviewed and discussed. The studies looking at the neurobiological effects of psychotherapy is reviewed and discussed. The practical aspects of combination therapy are presented and problems inherent are indicated and the effectiveness presented. Finally the Canadian Psychiatric Association's comprehensive evidence based clinical guideline for the treatment of depressive disorders is discussed as it relates to combination treatment. The conclusion is that an expanding body of evidence in the use of psychotherapy and psychopharmacology in combination is guiding us. As further studies are done, clearer guidelines will emerge leading to improve evidence-based practice of these modalities.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".