Magnitude and correlates of the intention-behavior gap in hematologic cancer survivors: An application of the multi-process action control framework
Bibliographic record
Abstract
Background: Efforts to help cancer survivors meet recommended aerobic exercise guidelines report modest success, perhaps because many theory-based interventions focus on intention formation. Only about half of survivors intending to meet exercise guidelines translate their intentions into guideline adherence. Thus, understanding the determinants of both intention formation and translation is important. The multi-process action control (M-PAC) framework proposes that the theory of planned behavior explains intention formation but additional regulatory behaviors (planning, regulation of alternatives), and reflexive factors (sense of obligation, regret, investment) are needed to translate intentions into behavior. Purpose: To explore the determinants of aerobic exercise intention formation and translation in hematologic cancer survivors. Methods: Hematologic cancer survivors (N=606) completed surveys reporting their aerobic exercise motivation and participation. The determinants of intention formation and translation were analyzed using separate logistic regressions. Results: Overall, 71% of participants (n=428) intended to exercise, and 60% of intenders (n=256) met exercise guidelines. The independent correlates of intention formation (all ps
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".