Principles for training in evidence‐based psychology: Recommendations for the graduate curricula in clinical psychology.
Bibliographic record
Abstract
We argue that the evidence-based practice (EBP) model represents an evolution in integrating science and practice and synchronizes well with broader trends in health care. Because the curriculum for EBP training involves explicit emphasis on the best empirical evidence within Clinical Psychology, it can be utilized by all programs, irrespective of theoretical orientation or training mission. We articulate four principles that speak to core training and foundational clinical supervision, to guide training using an EBP model. These principles can be integrated within the larger rubric of a program and can encourage more consistent curricular reliance on EBP. This approach to doctoral training could lead to greater consistency across training programs and bring science and practice closer together within Clinical Psychology.
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 | 0.125 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".