Proposal to Develop a Clinical Practice Guideline on Emotion Regulation: Final Report
Bibliographic record
Abstract
The Advisory Steering Committee for Development of Clinical Practice Guidelines created a working group to consider the feasibility of, and appropriate methods for, developing an APA clinical practice guideline for interventions that address a transdiagnostic change process, rather than a categorical disorder. Specifically, the working group was asked to determine whether development of a clinical practice guideline focused on emotion regulation was worth pursuing at this time, given the state of the scientific literature, feasibility, and considerations of clinical need and utility.The working group identified a set of goals for a clinical practice guideline based on the idea that if a guideline could mostly achieve these goals, it would be an advance for the field and worth the investment to try to develop a clinical practice guideline based on a transdiagnostic change process or principle.Goals:1) Inform providers, patients and their families, payers and other stakeholders about what the empirical data indicate regarding the efficacy of treatments targeting emotion regulation2) Enhance clinical utility of a clinical practice guideline so we learn more than typical guidelines tell us about:●What works, for whom, and under what circumstances ●What is known about widely-used treatments for which there is not rigorous evidence concerning efficacy ●What is known about change processes or principles underlying the effects of efficacious treatmentsAfter careful consideration of the opportunities and challenges likely to arise in developing a clinical practice guideline on emotion regulation, the working group recommends developing and publishing three review protocols that would lead to a clinical practice guideline with three components:Review 1. Systematic review of the efficacy data leading to recommendations, following the current best practices to reduce bias (and acknowledgement of biases not addressed with this approach). This would include the Population, Intervention, Comparator, Outcomes, Timing, and Setting (PICOTS) questions to assess applicability based on current best practices, and would include the usual review of literature on harms, patient preferences, etc. Note: Only in unusual cases when there is a good reason a priori to think that a given subgroup will have a unique response to a treatment will subgroup analyses, cohort or other studies (that don’t meet best practices to reduce bias) be included. In these cases, careful attention to likely confounds and differences in the analytic approach will need to be considered if these studies are used to guide recommendations.Review 2. Summary of current knowledge (with appropriate caveats about the quality of evidence) for select treatments that were not included in Review 1, when the treatments are both widely used and studies meet stringent, pre-specified inclusion and exclusion criteria.Review 3. Summary of current knowledge (with appropriate caveats about the quality of evidence) for literature on change processes or principles based on systematic inclusion and exclusion criteria to determine (observational) studies that rigorously try to account for confounding.This report outlines the working group’s process, rationale for a guideline on emotion regulation, review of challenges likely to arise when developing a guideline of this nature and how recognition of these challenges guided the current proposal. Next the proposal and rationale for the three reviews are discussed, along with consideration of how to manage scope and why the working group believes developing this guideline would be very challenging, but ultimately feasible. We close with recommendations for future research in the hopes that the available literature will one day better match the urgent clinical needs.
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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 | 0.304 | 0.482 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.073 | 0.048 |
| Insufficient payload (model declined to judge) | 0.022 | 0.022 |
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".