Exploring and prioritizing content to include in a medication self-management toolkit for persons with spinal cord injury/dysfunction: A concept mapping approach
Bibliographic record
Abstract
BACKGROUND: Adults with spinal cord injury/dysfunction (SCI/D) face challenges with medications they take to manage their secondary conditions (e.g., pain, urinary tract infections, autonomic dysreflexia). With many healthcare providers typically involved in care, there are additional challenges with care fragmentation and self-management. Prior research emphasized the desire for more support with medication self-management among this population. OBJECTIVE: To explore what content should be included in a medication self-management resource (i.e., toolkit) for adults with SCI/D, as well as considerations for delivery from the perspectives of adults with SCI/D, caregivers, healthcare providers, and representatives from community organizations. METHODS: A concept mapping study was conducted. Participants took part in one or more of three activities: brainstorming; sorting and rating; and mapping. Participants generated ideas about the content to include in a medication self-management toolkit. Participants sorted the statements into conceptual piles and assigned a name to each. All statements were rated on a five-point Likert-type scale on importance and realistic to include in the toolkit. Participants decided on the final cluster map, rearranged statements, and assigned a name to each cluster to create visual representations of the data. RESULTS: Forty-four participants took part in this study. The final map contained eight clusters: 1) information-sharing and communication; 2) healthcare provider interactions and involvement; 3) peer and community connections; 4) supports and services for accessing prescription medications and medication information; 5) information on non-prescription medication and medication supplies; 6) safety and lifestyle considerations; 7) general medication information; and 8) practical information and strategies related to medication-taking. Safety and lifestyle considerations was rated as the most important and realistic to include in the toolkit. CONCLUSIONS: Given the limited tools to help adults with SCI/D with managing their medications, there is great potential to better support this population across all areas of medication self-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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".