The Contribution of Treatment Allocation Method to Outcomes in Intervention Research
Bibliographic record
Abstract
The purpose of this methodological study was to examine the contribution of treatment allocation method (random vs. preference) on the immediate, intermediate, and ultimate outcomes of a behavioural intervention (MCI) for insomnia. Participants were allocated to the MCI randomly or by preference. Outcomes were assessed before, during, and after completion of the MCI using validated self-report measures. Analysis of covariance was used to compare the post-test outcomes for the 2 groups, controlling for baseline differences. Compared to those randomized, participants in the preference group showed improvement in most immediate outcomes (sleep onset latency, wake after sleep onset, sleep efficiency), both intermediate outcomes (insomnia severity and daytime fatigue), and one ultimate outcome (resolution of insomnia). Using a systematic method for eliciting participants' preferences and involving participants in treatment selection had a beneficial impact on immediate and intermediate outcomes. Additional research should validate the mechanism through which treatment preferences contribute to outcomes.
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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 | 0.564 | 0.741 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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; 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".