Effects of the quality of implementation intentions for physical activity on achieving weight loss
Bibliographic record
Abstract
The purpose of the current study is to investigate the effects of implementation intentions (IIs) for physical activity on weight loss in an overweight/obese population (BMI range of 28 to 45 kg/m2, waist circumference?=?88 for women, = 102 for men) partaking in the McGill CHIP Healthy Weight Program, a lifestyle behaviour change program. We hypothesize that higher specificity of the IIs (i.e., degree of II detail) leads to greater weight loss. Furthermore, we will explore whether plans for unstructured physical activity will be more or less effective for weight loss than plans for structured physical activity. Specifically, structured plans require setting time aside specifically for the planned activity, whereas unstructured plans are plans where the physical activity done in conjunction within another daily activity. A total of 454 IIs from 35 participants who have completed at least 12 weeks of the program will be coded and analyzed. The study is in the coding phase, which is done by two separate coders who rate each II for degree of specificity and whether it refers to a structured or unstructured plan. The analyses will commence following the completion of the coding.Acknowledgments: I wish to express my sincere gratitude to Dr. Bärbel Knäuper for giving me the incredible opportunity to pursue this project, and for her constant support and encouragement. I also wish to thank Elena Ivanova and Zhen Xu for their continuous guidance and generosity.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".