Toward an Evidence-based Standard of Professional Competence
Bibliographic record
Abstract
Abstract Psychotherapists are ethically bound to provide services within the boundaries of their competence traditionally delimited by their education, training, and supervised experience. Throughout this chapter, two historical examples illustrate the shortcomings of the current standard as well as the promise of an alternative. The guidelines now in place are critiqued in light of the empirical evidence. The authors propose that effectiveness become the foundation of any formulation and assessment of competence. Developments over the last two decades make it possible for clinicians to measure their results and compare them to international norms—a process known as routine outcome monitoring. However, mere measurement and comparison to benchmarks are insufficient. To be ethical, to protect public welfare, practitioners must also act on the data provided by routine outcome monitoring. Using feedback informed treatment and deliberate practice therapists can both enhance their responsiveness to individual clients and continuously improve their outcomes. Challenges of implementation are discussed. A case study of an agency that successfully adopted routine outcome monitoring coupled with deliberate practice using best practices gleaned from the implementation science literature is offered.
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 | 0.312 | 0.359 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".