Adapting TeachABI to the local needs of Australian educators – a critical step for successful implementation
Bibliographic record
Abstract
Background The present study is the foundational project of TeachABI-Australia , which aims to develop and implement an accessible, nation-wide digital resource for educators to address their unmet acquired brain injury (ABI)-related professional learning needs. The aim of the present study was to identify the adaptations required to improve the suitability and acceptability of the TeachABI professional development module within the Australian education system from the perspectives of Australian educators. Methods The research design employed an integrated knowledge translation approach and followed the ADAPT Guidance for undertaking adaptability research. A purposive sample of eight educators eligible to teach primary school in Australia provided feedback on the module through a quantitative post-module feedback questionnaire and a qualitative semi-structured interview. Results Participants rated the acceptability of the module as 'Completely Acceptable ' (Mdn = 5, IQR = 1), and reported 'only Minor' changes were required (Mdn = 2, IQR = 0.25) to improve the suitability to the Australian context. Qualitative analysis of transcripts revealed three broad categories: (1) the usefulness of TeachABI , (2) the local fit of TeachABI , and (3) pathways for implementing TeachABI in the local setting. Recommended adaptations to the module collated from participant feedback included changes to language, expansion of content, and inclusion of Australian resources, legislation, and videos. Conclusions TeachABI is acceptable to Australian educators but requires modifications to tailor the resource to align with the unique schooling systems, needs, and culture of the local setting. The systematic methodological approach to adaptation outlined in this study will serve as a guide for future international iterations of TeachABI .
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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 | 0.135 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".