Exploratory randomized controlled trial evaluating the impact of a waiting list control design
Bibliographic record
Abstract
BACKGROUND: Employing waiting list control designs in psychological and behavioral intervention research may artificially inflate intervention effect estimates. This exploratory randomized controlled trial tested this proposition in a study employing a brief intervention for problem drinkers, one domain of research in which waiting list control designs are used. METHODS: All participants (N = 185) were provided with brief personalized feedback intervention materials after being randomly allocated either to be told that they were in the intervention condition and that this was the intervention or to be told that they were in the waiting list control condition and that they would receive access to the intervention in four weeks with this information provided in the meantime. RESULTS: A total of 157 participants (85%) were followed-up after 4 weeks. Between-group differences were found in one of four outcomes (proportion within safe drinking guidelines). An interaction was identified between experimental manipulation and stage of change at study entry such that participant change was arrested among those more ready to change and told they were on the waiting list. CONCLUSIONS: Trials with waiting list control conditions may overestimate treatment effects, though the extent of any such bias appears likely to vary between study populations. Arguably they should only be used where this threat to valid inference has been carefully assessed.
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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 | 0.075 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".