Student Competition (Knowledge Generation) ID 1985151
Bibliographic record
Abstract
Background Persons with traumatic spinal cord injury (SCI) are often experience polypharmacy, the use of multiple medications, to manage secondary complications and concurrent conditions. Despite the prevalence of polypharmacy and challenges associated with managing medications, there are few tools to support persons with SCI with medication self-management. 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 Articles were searched on electronic databases and grey literature. For inclusion, articles were required to include an adult population with a traumatic SCI and an intervention targeting medication management. They had to incorporate a component of self-management. Articles were independently screened and data were extracted and synthesized using descriptive approaches. Findings Three studies were included in this scoping review. Interventions included a mobile app and two education-based interventions to address self-management of SCI, medication management, and pain management. None of the identified interventions addressed medication self-management comprehensively. Learning outcomes (perceived knowledge and confidence), behavioural outcomes (management strategies, data entry), and clinical outcomes (number of medications, pain scores, functional outcomes) were evaluated. Results of the interventions varied, but some positive outcomes were noted, with improvements in perceived knowledge and confidence and a reduction in the use of multiple pain medications. Conclusion Overall, there are limited interventions targeting medication self-management for persons with SCI. There is an opportunity better support this population through the co-design and implementation of an intervention that comprehensively addresses self-management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.886 | 0.600 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".