Effect of pretesting on intentions and behaviour: A pedometer and walking intervention
Bibliographic record
Abstract
This study addressed the influence of pedometers and a pretest on walking intentions and behaviour. Using a Solomon four-group design, 63 female university students were randomly assigned to one of four conditions: pedometer and pretest (n = 16), pedometer and no pretest (n = 16), no pedometer and pretest (n = 15), no pedometer and no pretest (n = 16). The pretest conditions included questions on walking, intentions to walk 12,500 steps per day, and self-efficacy for walking 12,500 steps per day. In the pedometer conditions a Yamax Digi-Walker SW-650 pedometer was worn for one week. All participants completed posttest questions. While significant pretest x pedometer interactions would have indicated the presence of pretest sensitisation, no such interactions were observed for either intention or self-reported walking. Wearing pedometers reduced intentions for future walking and coping self-efficacy. However, after controlling for pretest self-reported walking, pedometer use resulted in more self-reported walking. We conclude that wearing a pedometer increased self-reported walking behaviour but that a pretest did not differentially influence walking intentions, behaviour, or self-efficacy.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".