EMPIRICAL VALIDATION AND PROFESSIONAL CODES OF ETHICS: DESCRIPTION OR PRESCRIPTION?
Bibliographic record
Abstract
Codes of ethics have begun to refer to preferences that interventions be “empirically supported.” The movement toward Empirically Supported Treatments (ESTs) is based on a medical model of intervention using randomized controlled trials as the prime method, one originally developed to test the efficacy of educational reforms and, later in the twentieth century, of medical interventions. It presumes standardized, “objective” procedures that demonstrate interventions in an unambiguous manner. As a methodology it limits the demonstrable efficacy of alternative forms of psychotherapy. Alternative practices, including constructivist approaches, are at risk of being considered unethical within this framework because their practitioners are unlikely to conduct the kind of outcome research that is considered necessary to demonstrate efficacy (e.g., APA Division 12 criteria). This article will consider the historical context of the professionalization of psychotherapy, the development of modes of outcome research, and the role of institutional texts such as ethics codes. The latter can act as regulatory tools guarding the autonomy of the psychology profession, and responding to the pressures of the market to maintain psychology's self-regulatory role. The move toward placing ESTs within codes of ethics is premised on limited conceptions of therapy that, we argue, fail to grasp the moral nature of the therapeutic relationship.
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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 | 0.275 | 0.525 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.005 | 0.126 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".