How sharp are the tools? Consumer perceptions of the SCI Get Fit Toolkit
Bibliographic record
Abstract
The SCI Get Fit Toolkit is an evidence-based resource that was recently launched to aid in the dissemination of the physical activity (PA) guidelines for individuals with spinal cord injury (SCI). This qualitative pilot study was a part of a larger project designed to develop and evaluate the SCI Get Fit Toolkit. The objectives were to evaluate the suitability of the SCI Get Fit Toolkit for promoting PA among individuals with SCI, and obtain participants' perceptions of the Toolkit in order to make modifications that would increase the effectiveness of the resource for encouraging PA. Semi-structured interviews were conducted via telephone or focus group with 9 individuals with SCI. Thematic analyses of the transcribed data were used to identify patterns in responses. All 9 participants evaluated the Toolkit as suitable for healthy individuals with SCI not currently meeting the PA guidelines. However, participants identified concerns that were captured by three themes: Toolkit design, heterogeneous suitability, and activity accessibility. Specifically, participants indicated a need for bigger font size and expanded content for comprehensiveness. Second, the Toolkit did not include suitable photos and activities for older individuals and those with higher level injuries. Third, there were concerns about the accessibility of some of the suggested activities (e.g. swimming) which may not be available for the majority of the target audience. Results of the qualitative analysis were used to modify the SCI Get Fit Toolkit by increasing font size, including images relevant to a diverse population, and providing more detailed information and resources on the SCI Action Canada website.Acknowledgments: Funding: Rick Hansen Institute, the Ontario Neurotrauma Foundation, and the Canadian Paralympic Committee
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".