Get In Motion 2.0: The relationship between the implementation and effectiveness of a real-world leisure-time physical activity telephone counseling service for adults with spinal cord injury
Bibliographic record
Abstract
Get in Motion (GIM) is an evidence- and theory-based telephone counseling service implemented under real-world conditions that promotes leisure-time physical activity participation (LTPA) among Canadian adults with spinal cord injury (SCI). The first phase of GIM (June 2008-June 2011) was shown to be effective at sustaining intentions for, and increasing participation in, LTPA; however, it is unclear how the first phase of the service led to these outcomes. The purpose of the current study was to explore the implementation correlates of change in LTPA intentions and behaviour among clients who enrolled in the second phase of the GIM service (September 2011-January 2014). The counselor tailored counseling session frequency, duration, and content to meet clients’ (n = 46; 50.0% male; 50.0% paraplegia; 51.46 ± 12.36 years old) LTPA interests, needs, and preferences. Implementation information about intervention dose and content was monitored using Counseling Session Checklists. Clients’ were asked to self-report their intentions for and actual aerobic and strength-training LTPA participation at baseline, 2-, 4-, and 6-months. Clients who completed the 6-month service were also asked to report their perceptions of service quality. Results demonstrated that the second phase of GIM was effective at sustaining LTPA intentions and increasing time spent in moderate-to-vigorous strength-training and total LTPA among clients. Increases in clients’ moderate-to-vigorous aerobic LTPA were significantly positively related to intervention dose and content. Clients’ perception of the service’s credibility was also significantly positively related to changes in moderate-to-vigorous aerobic LTPA over the 6-month service. This study provides additional knowledge that will contribute to the refinement of GIM as it enters its third action cycle.Acknowledgments: Funding for the Get in Motion service was provided by the Rick Hansen Institute and the Ontario Neurotrauma Foundation. This work was supported by an Ontario Neurotrauma Foundation and Rick Hansen Institute Mentor-Trainee Capacity Building Award in Knowledge Mobilization awarded to JRT and KMG. The authors would like to acknowledge SCI Ontario and SCI British Columbia for their assistance with promoting the GIM service, as well as Clement Lau, Krystina Malakovski, Krystn Orr, Christine Palisoc, Maryam Somo, Jessie Stapleton, and Matthew Stork for their assistance with data collection and management.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".