Planning and Developing a Physical Activity mHealth Intervention in Partnership: SCI Step Together
Bibliographic record
Abstract
Individuals living with spinal cord injury (SCI) who walk participate in less physical activity than individuals with SCI who use wheelchairs. Lower levels of physical activity may be due to barriers such as increased pain and fatigue, lack of time, and lack of knowledge. Additionally, in the second study of my dissertation, we identified that action and coping planning, goal conflict, and skills are associated with increased physical activity levels. Quality of physical activity experiences also play an important role in participation. Recently, elements of positive and negative physical activity experiences, in addition to factors that influence quality, were classified among individuals with SCI who ambulate in the third study of my dissertation. These elements mapped onto the Quality Participation Framework (i.e., autonomy, belongingness, challenge, engagement, meaning, mastery) and the conditions aligned with the Quality Parasport Participation Framework (intrapersonal, social, program, and physical). Despite the recent focus on understanding factors that enhance both quantity and quality of physical activity participation, there are no physical activity interventions available targeted specifically for individuals with SCI who walk. Therefore, this study will develop and assess the acceptability and feasibility of a mHealth app to increase the quantity and quality of physical activity among ambulators with SCI according to evidence from the previous two studies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".