Ain’t necessarily so: Review and critique of recent meta-analyses of behavioral medicine interventions in health psychology.
Bibliographic record
Abstract
OBJECTIVE: We examined four meta-analyses of behavioral interventions for adults (Dixon, Keefe, Scipio, Perri, & Abernethy, 2007; Hoffman, Papas, Chatkoff, & Kerns, 2007; Irwin, Cole, & Nicassio, 2006; and Jacobsen, Donovan, Vadaparampil, & Small, 2007) that have appeared in the Evidence Based Treatment Reviews section of Health Psychology. DESIGN: Narrative review. MAIN OUTCOME MEASURES: We applied the following criteria to each meta-analysis: (1) whether each meta-analysis was described accurately, adequately, and transparently in the article; (2) whether there was an adequate attempt to deal with methodological quality of the original trials; (3) the extent to which the meta-analysis depended on small, underpowered studies; and (4) the extent to which the meta-analysis provided valid and useful evidence-based recommendations. RESULTS: Across the four meta-analyses, we identified substantial problems with the transparency and completeness with which these meta-analyses were reported, as well as a dependence on small, underpowered trials of generally poor quality. CONCLUSION: Results of our exercise raise questions about the clinical validity and utility of the conclusions of these meta-analyses. Results should serve as a wake up call to prospective authors, reviewers, and end-users of meta-analyses now appearing in the literature.
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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 | 0.313 | 0.612 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| 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; 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".