Does participation in an intervention affect responses on self-report questionnaires?
Bibliographic record
Abstract
There has been some concern that participation in an intervention and exposure to a measurement instrument can change participants' interpretation of the items on a self-report questionnaire thereby distorting subsequent responses and biasing results. Differential item functioning (DIF) analysis using item response modeling can ascertain possible differences in item interpretation by testing for differences in item location between groups. The DIF for treatment versus control group differences at post-intervention assessment and the Time 1 and Time 2 differences in a control group were analyzed using data from a dietary change intervention trial for Boy Scouts. The measures included fruit and vegetable (FV) frequency of consumption, preferences and self-efficacy. Treatment-control group DIF at post-intervention assessment was detected in a higher percentage of items for FV frequency than for preference or self-efficacy. Time 1 to Time 2 differences in items for the control group were detected in one item for each of the three scales. Further research will need to clarify whether the obtained DIFs reflected true changes in frequency, preference or self-efficacy or some reinterpretation of items by participants following an intervention or merely after previous exposure to the measure.
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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 | 0.098 | 0.264 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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; 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".