Applying test operating characteristics to measures of exercise motivation: A primer
Bibliographic record
Abstract
Physical activity programmes that include a motivational counselling component can be effective at increasing exercise participation. Reliable screening procedures could provide a cost effective method of identifying and channelling those 'at risk' for non-participation into a motivational counselling intervention and increase participation and long-term adherence. Traditional statistical methods have played an important step in developing measures that are good predictors of future exercise behaviour. Test operating characteristics (TOCs), a set of clinical outcome statistics, could be used to evaluate the accuracy of these measures as screening tools in identifying those 'at risk' for non-participation and those 'not at risk' for use in applied settings, such as physical activity programmes. This paper will provide a primer on the use of TOCs, particularly as they apply to the evaluation of measures of exercise motivation. Also provided is an example application using eight measures of exercise motivation previously used in research.
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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 | 0.047 | 0.100 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".