Exploring the adaptability of TeachABI as an online professional development module for high school educators
Bibliographic record
Abstract
Educators often lack the knowledge and resources to assist students with acquired brain injury (ABI). Teach-ABI, an education module, was created to help elementary school teachers support students with ABI in classrooms. This study examined the adaptability of Teach-ABI for high school educators. A qualitative descriptive study explored high school educators' (n = 9) experiences reviewing Teach-ABI and its adaptability for high school through semi-structured interviews. The interview guide was informed by implementation and adaptation frameworks. Transcripts were examined using directed content analysis. Teachers felt Teach-ABI was a good foundation for creating a high school-based education module. Adaptations were highlighted, such as streamlining content (e.g., mental health) and strategies (e.g., supporting test taking), to better meet educator needs. Using implementation science and adaptation frameworks provided a structured approach to explore the adaptive elements of Teach-ABI. The module was perceived as a suitable platform for teaching high school educators about ABI. Teach-ABI is an innovative, user informed education module, providing a multi-modal (e.g., case study, videos) and replicable approach to learning about ABI. Applying frameworks from different fields provides concepts to consider when tailoring resources to align with educator needs (e.g., grade, class environment) and facilitate innovation uptake.
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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".