Views of people with traumatic spinal cord injury about the components of self-management programs and program delivery: a Canadian pilot study
Bibliographic record
Abstract
BACKGROUND: Given the increasing emphasis on the community management of spinal cord injury (SCI), strategies that could be developed and implemented in order to empower and engage individuals with SCI in promoting their health and minimizing the risk of health conditions are required. A self-management program could be one approach to address these complex needs, including secondary complications. Thus, the objective of this study was to determine the importance attributed to the components of a self-management program by individuals with traumatic SCI and explore their views/opinions about the delivery of such a program. METHODS: Individuals with SCI were recruited by email via the Rick Hansen Institute (Vancouver, British Columbia, Canada) as well as an outpatient hospital spinal clinic. Data were collected by self-report using an on-line survey. RESULTS: The final sample size was 99 individuals with traumatic SCI. The components of a self-management program that were rated as "very important" by the greatest proportion of participants included: exercise (n= 53; 53.5%), nutrition (n= 51; 51.5%), pain management (n= 44; 44.4%), information/education on aging with a SCI (n= 42; 42.4%), communicating with health care professionals (n= 40; 40.4%), problem solving (n= 40; 40.4%), transitioning from rehabilitation to the community (n= 40; 40.4%), and confidence (n= 40; 40.4%). Overall, 74.7% (n= 74) of the sample rated the overall importance of the development of a self-management program for individuals with traumatic SCI as "very important" or "important". Almost 40% (n= 39) of the sample indicated that an internet-based self-management program would be the best delivery format. The highest proportion of participants indicated that the program should have individuals of a similar level of injury (n= 74; 74.7%); having individuals of a similar age (n= 40; 40.4%) was also noted. Over one-quarter of the sample (n= 24) had a depression score consistent with significant symptoms of depression. CONCLUSIONS: Future research is needed to further evaluate how the views of people with traumatic SCI change over time. Our findings could be used to develop and pilot test a self-management program for individuals with traumatic SCI.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".