Evidence-Based Practice: Separating Science from Pseudoscience
Bibliographic record
Abstract
Evidence-based practice (EBP) requires that clinicians be guided by the best available evidence. In this article, we address the impact of science and pseudoscience on psychotherapy in psychiatric practice. We describe the key principles of evidence-based intervention. We describe pseudoscience and provide illustrative examples of popular intervention practices that have not been abandoned, despite evidence that they are not efficacious and may be harmful. We distinguish efficacy from effectiveness, and describe modular approaches to treatment. Reasons for the persistence of practices that are not evidence based are examined at both the individual and the professional system level. Finally, we offer suggestions for the promotion of EBP through clinical practice guidelines, modelling of scientific decision making, and training in core skills.
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 | 0.101 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.036 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".