Stages of motivational readiness for physical activity: A comparison of different algorithms of classification
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to compare two approaches to classify individuals into stages of motivational readiness for physical activity and test which one was better explained by attitude and perceived behavioural control, as defined by Ajzen (1991). DESIGN: A survey of 20,430 respondents from a population-based sample. METHODS: The relevant variables were assessed in a self-administered questionnaire. The cluster approach consisted of combining both intention and behaviour in order to determine clusters of individuals; such clusters correspond to different stages of motivational readiness. The stage of change (SC) approach consisted of grouping the same individuals by using the SC variable of the Transtheoretical Model (TTM). RESULTS: The SC and cluster-solution approaches were replicated across four subsamples of the total number of respondents. Attitude and perceived behavioural control were more strongly associated with stage membership derived from four-cluster solution than with stage membership in the five categories assessed by the SC method. CONCLUSION: Stage of motivational readiness for physical activity, and possibly for other health-related behaviours, may usefully be characterized when both recent past behaviour and intention in the near future are simultaneously and explicitly taken into consideration.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".