“Gender matters”: the development of infographics to raise awareness and promote gender-transformative care in traumatic brain injury
Bibliographic record
Abstract
PURPOSE: To develop a series of infographics providing persons with traumatic brain injury (TBI) and their circle of care with evidence-based information on sex and gender topics in TBI. MATERIALS AND METHODS: We employed an iterative participatory design engaging knowledge users, scientists, and experts in brain injury and patient education. To inform infographic content, we conducted an information needs assessment with knowledge users through semi-structured interviews and referred to our previously published evidence syntheses on TBI topics. We followed principles of graphic design and science communication to create materials reflecting lived experiences of knowledge users. RESULTS: We created a series of infographics with actionable messages and visual representations of evidence-based information. We achieved a Flesch Reading-Ease score of 60.1, corresponding to a Grade 7/8 reading level. The infographics met the color contrast criteria of the Web Content Accessibility Guidelines. Knowledge users found the material useful, visually appealing, and helpful in understanding complex topics. CONCLUSIONS: There is value in merging art and science to develop educational materials that meet the unique information needs of knowledge users. Iterative participatory design engaging diverse stakeholders is essential for co-creating knowledge translation tools to improve access to health information and quality of care after TBI.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".