A scoping review of medication self-management intervention tools to support persons with traumatic spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Persons with traumatic spinal cord injury (SCI) use multiple medications (polypharmacy) to manage the high number of secondary complications and concurrent conditions. Despite the prevalence of polypharmacy and challenges associated with managing medications, there are few tools to support medication self-management for persons with SCI. OBJECTIVE: The purpose of this scoping review was to identify and summarize what is reported in the literature on medication self-management interventions for adults with traumatic SCI. METHODS: Electronic databases and grey literature were searched for articles that included an adult population with a traumatic SCI and an intervention targeting medication management. The intervention was required to incorporate a component of self-management. Articles were double screened and data were extracted and synthesized using descriptive approaches. RESULTS: Three studies were included in this review, all of which were quantitative. A mobile app and two education-based interventions to address self-management of SCI, medication management, and pain management, respectively, were included. Only one of the interventions was co-developed with patients, caregivers, and clinicians. There was minimal overlap in the outcomes measured across the studies, but learning outcomes (e.g., perceived knowledge and confidence), behavioural outcomes (e.g., management strategies, data entry), and clinical outcomes (e.g., number of medications, pain scores, functional outcomes) were evaluated. Results of the interventions varied, but some positive outcomes were noted. CONCLUSIONS: There is an opportunity to better support medication self-management for persons with SCI by co-designing an intervention with end-users that comprehensively addresses self-management. This will aid in understanding why interventions work, for whom, in what setting, and under what circumstances.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".