Intention-Behavior Relationship Based on Epidemiologic Indices: An Application to Physical Activity
Bibliographic record
Abstract
PURPOSE: This article examines the usefulness of epidemiologic indices in furthering the understanding of the intention-behavior relationship in the field of physical activity. DESIGN: Six prospective data sets of physical activity were used. SETTING: The United Kingdom and Canada in various settings (school, workplace). SUBJECTS: Different segments of the population (students, employees). MEASURES: Intention at baseline and behavior at follow-up, both assessed by means of questionnaires. ANALYSIS: Intention and behavior were dichotomized to create a 2 x 2 table; this allowed us to compute four standard epidemiologic indices: sensitivity, specificity, positive predictive value (PV+), and negative predictive value (PV-). RESULTS: Sensitivity was 86.3%, which reflected the high sensitivity of intention for exercising, i.e., active individuals were very likely to hold a positive intention. Specificity was 49.5%, which suggested that a significant number of inactive individuals held a positive intention. With respect to predictive values, a low intention was a very good predictor of being inactive (PV- = 88.1%), whereas a positive intention was a moderate predictor of being active (PV+ = 45.5%). CONCLUSION: These results indicate that intention is a moderate predictor of behavior and that the gap between intention and behavior is caused by high intenders not taking action. Health promotion programs would benefit to target factors that moderate the intention-behavior relationship.
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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 | 0.019 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".