Student Competition (Knowledge Generation) ID 1985014
Bibliographic record
Abstract
Background Adults with spinal cord injury/dysfunction (SCI/D) are commonly prescribed multiple medications to manage secondary complications. Significant challenges managing medications have been highlighted, with the need for more support with medication self-management. Objective The objective of this study is to co-develop a toolkit to assist with medication self-management for persons with SCI/D. Methods Adults with SCI/D, caregivers, and healthcare providers will participate in the three steps of concept mapping – brainstorming, sorting and rating, and mapping to identify key components of the toolkit. Participants will generate statements about what should be incorporated into a toolkit to help persons with SCI/D manage their medications. Participants will rate the final list of statements on importance and feasibility and sort the statements into thematic piles. A visual map will be developed by a subset of participants, representing the thematic piles. Findings To date, participants have generated over 500 statements. Ideas generated around the content of the toolkit focus on information about: pharmacological and non-pharmacological options for managing secondary complications, side effects, communicating with providers, and medication access. Ideas specific to the delivery of the toolkit focus on: ensuring an individualized approach, accessibility, and the use of visuals. Statements will be synthesized for sorting and rating and mapping. Conclusion Subsequent phases of this research will refine the toolkit through interviews and input from our working group. A mixed methods pilot evaluation will then be conducted to assess the feasibility, acceptability, and appropriateness of the toolkit, as well medication knowledge, self-efficacy, and quality of life.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.845 | 0.604 |
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".