In a class of their own: Predicting physical activity trajectories among people with spinal cord injury
Bibliographic record
Abstract
Objective: It is crucial to understand long-term leisure time physical activity (LTPA) patterns of persons with spinal cord injury (SCI) as the challenges of living with this disability heavily influence LTPA levels. The purpose of this study was to explore emerging LTPA patterns in a sample of persons with SCI over an 18-month period. In addition, the study aimed to investigate the influence of pressure ulcers, demographic variables, and theory of planned behaviour (TPB) constructs on the emerging LTPA trajectories. Methods: Participants (N = 541) were enrolled in the SHAPE-SCI study and responded to questionnaires assessing LTPA, TPB constructs and demographic variables. Latent Class Growth Modeling was used to detect emerging LTPA patterns and to test the influence of potential predictors. Results: Four LTPA patterns emerged: inactive, increaser, decreaser and stable active, representing 22%, 14%, 32% and 32% of the sample respectively. The presence of pressure ulcers resulted in a decline in LTPA among participants with a stable active trajectory. Finally, LTPA intentions were higher in all patterns compared to the inactive group. Injury severity, age and years post-injury also were related to the trajectories. Conclusion: Interventions should focus on increasing individuals' intentions and should be directed towards people who are older, have more severe injuries and have been injured for longer.Acknowledgments: We would like to acknowledge the SHAPE-SCI Research Group. Research support was obtained from an Operating Grant from the Canadian Institutes of Health Research (grant no. MOP 57778).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".