Avoidable pitfalls in behavioral medicine outcome research
Bibliographic record
Abstract
To secure the role of behavioral medicine in health care, researchers continue to improve the quality of their outcome studies. Despite the availability of guidelines for designing high quality clinical trials, however, we have noted two, unfortunately common, flaws in behavioral medicine outcome research that undermine these efforts. The first issue is that researchers recruit medical patients whose scores on psychological target measures are not elevated at pretest. Data are presented from quantitative reviews of cardiovascular and cancer populations to illustrate the impact of this protocol decision. It is demonstrated how magnitude of change and corresponding statistical power are greatly reduced when patients with few problems are enrolled. The second issue pertains to the failure of researchers to measure psychological change when the actual model to be tested is a mediational model such that successful treatment of psychological distress is presumed to account for good long-term health outcomes. Such lack of attention to protocol design can result in misinterpretation of obtained effects and can lead to premature dismissal of psychological treatment opportunities for physical disease. We suggest how these flaws can be avoided in the protocol design stage.
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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 | 0.850 | 0.853 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".