Experimental Evaluation of the Impact of Lived Experience and Personal Story on Neuroscience Knowledge Translation Effectiveness: Sharing the Neuroscience of <scp>ADHD</scp> with <scp>Pre‐Service</scp> Teachers
Bibliographic record
Abstract
ABSTRACT Previous work suggested that sharing personal stories is effective for knowledge translation (KT) of the neuroscience of attention deficit hyperactivity disorder (ADHD) for a teacher audience. In the current study, we experimentally evaluated the impact of personal story and lived experience on a similar KT activity. We measured knowledge and attitudes about ADHD before and after our KT activity and used a factorial design to evaluate the impact of personal story (personalized versus depersonalized) and lived experience (presenter with versus without an ADHD diagnosis) with N = 14 to 24 per group. The presenter without an ADHD diagnosis was a neuroscience expert. All conditions were associated with increased attribution of ADHD symptoms to the brain. Speaker quality ratings were high, especially in the personalized + ADHD diagnosis condition and the depersonalized + no ADHD diagnosis condition. While incorporating lived experience is important for authentic KT, we demonstrated that the KT presenter themselves need not have lived experience to change pre‐service teacher attitudes and beliefs. More work is needed to address the potential impacts of neuroscience expertise and other aspects of the presenters in our study.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".