Reducing overestimated intentions and expectations for physical activity: The effect of a corrective entreaty
Bibliographic record
Abstract
We assessed whether intentions and expectations formed in a hypothetical physical activity situation are different from those formed in a real situation; and whether the intentions and expectations of participants who are hypothetically given a free pass to attend a fitness class to better match their behaviour if they are administered a corrective entreaty (CE), than if they are not. In two separate studies, undergraduate university students were randomised into three groups: (a) Hypothetical (H); (b) Hypothetical with CE (HE) and (c) Real (R), and were asked to rate their intention and expectation to use a fitness pass. As hypothesised, significantly more participants expected that they would use the free fitness pass in the H group compared to the R group in both studies. Significantly fewer participants in the HE condition expected to use their free pass compared to the H group in Study 2. Also, significantly more corresponding expectation-behaviour relationships were found in the HE and R groups compared to the H group in both studies. Administering a CE influenced expectations formed in a hypothetical situation making them more similar to expectations formed in a real situation, and increased the specificity of tests of correspondence between expectation and behaviour.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".