Empirically supported treatments: will this movement in the field of psychology impact the practice of psychosocial oncology?
Bibliographic record
Abstract
This article discusses the likelihood of the empirically supported treatment (EST) movement impacting the practice of psychosocial oncology, with the goal of raising awareness of these issues and encouraging debate within the psychosocial oncology research and practice community. In 1993, the American Psychological Association struck a task force to develop criteria for empirically evaluating psychological interventions. The Clinical Psychology (Division 12) Task Force now evaluates psychological interventions and publishes an updated list of ESTs on a yearly basis. Concerns raised about the EST movement in psychology have included difficulties with the terminology and process of the Task Force, problems with the methodology used in psychotherapy research, and with the possible practical implications of the Task Force list of EST. A review of the literature suggests that psychosocial interventions in oncology are currently beginning to be evaluated by the EST criteria and that the reviewed interventions have yet to attain EST status. Following from this review, it is argued that researchers and practitioners in psychosocial oncology should become aware of the standards established by the Division 12 Task Force and that future psycho-oncology intervention research may need to be designed to meet those standards. The discipline of psychosocial oncology is encouraged to consider the possible implications of accepting or not accepting the EST criteria.
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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 | 0.119 | 0.187 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.023 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 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".