The Development of a Physical Activity Persona Profile Classification for Public Education Campaigns
Bibliographic record
Abstract
BACKGROUND: Historically, physical activity (PA) promotion campaigns have been evaluated with social cognitive approaches that are limited in explaining the PA intention-behavior gap and distinguishing between behavioral adoption and maintenance. The purpose of this research was to develop and test a simple classification that addresses PA intention-behavior translation and maintenance, using action control and dual-process theories with 4 items (PA intentions, behavior, habit, and identity). METHODS: A classification was developed on one population (n = 1350) and tested on 2 PA campaign populations (n = 376 and n = 6396). The a priori criterion for success was for the resultant profiles to each account for ≥10% of the sample and collectively account for ≥85% of the sample. RESULTS: The initial classification produced 4 profiles: (1) "nonintenders" (ie, low intentions, low PA, low habit, and low identity); (2) "unsuccessful adopters" (ie, high intentions, low PA, low habit, and low identity); (3) "successful adopters" (ie, high intentions, high PA, low habit, and low identity); and (4) "successful maintainers" (ie, high intentions, high PA, and high habit, or high identity); which collectively accounted for 87.6% of the sample. The 2 test samples revealed 2 profiles ("successful adopters" and "successful maintainers") that accounted for 69.8% and 70.9% of the samples, respectively. CONCLUSIONS: The classification produced 4 profiles; however, only 2 profiles were revealed in the test samples. These differences may have been the result of the PA campaign populations being more inclined toward translating intentions and maintaining behavior. Future research should assess profile distributions across diverse campaign audiences.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".