Discursive Psychology: An Alternative Approach for Studying Adherence to Exercise and Physical Activity
Bibliographic record
Abstract
Within exercise psychology, social cognitive theories have allowed researchers to identify possible influences and mechanisms that account for exercise and physical activity participation. These approaches have advanced the development of interventions to enhance and maintain exercise adherence. Despite this, the adherence problem remains unsolved. This paper introduces an alternative perspective known as discursive psychology. and explores its potential for understanding adherence. How this approach differs from leading approaches is highlighted. Discursive psychology's potential contribution via its focus on discourse and what is accomplished through people's use of words is considered in detail. How discursive psychology contributes to understanding exercise adherence by opening up new avenues of research and associated methodologies is also discussed. It is concluded this approach will complement and enhance existing approaches by focusing on how people are situated within discourses and how this affords and limits how they speak, feel, and behave with respect to exercise.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".