Improving attitudes towards breaks from sitting at home and at work: The role of structural and meta-cognitive attitude bases in the effectiveness of affective and cognitive messages
Bibliographic record
Abstract
"Sitting is the new smoking". This phrase has been repeated in news articles, TED talks, and even journal articles in an effort to draw a comparison between the degree to which excessive sitting and smoking negatively impact health, and subsequently persuade people to reduce their sitting time. While evidence detailing the negative effects of sitting is increasing, research on how to persuade people to reduce sitting time is severely lacking. To address this gap, this study investigated whether a match or mismatch between message type (affective/cognitive) and structural and meta-cognitive attitude bases (AB; affective/cognitive) would yield greater change in attitudes towards breaks from sitting at home and at work. Participants' (n=291) overall attitudes towards breaks, and affective and cognitive structural and meta-cognitive AB were assessed before and after participants were randomly assigned to view the cognitive or affective message. Hierarchical regressions that included message type, AB (structural or meta-cognitive), and their interaction as predictors of overall attitudes (home and work) were conducted. Results revealed a relative matching effect for attitudes (home): among participants with affectively-based attitudes, those who saw a matching message showed greater attitude change than those who saw a mismatching message (p0.05). For attitudes (work), none of the predictors were significant (p>0.05). These patterns of results were equivalent for structural and meta-cognitive AB. In conclusion, this study partially supports relative matching effects and indicates that matching/mismatching effects may not be uniform across contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".