Thinking Otherwise: Bringing Young People into Pediatric Concussion Clinical and Research Practice
Bibliographic record
Abstract
Abstract Background: As rates of pediatric concussion have steadily risen, and concerns regarding its consequences have emerged, pediatric concussion has received increased attention in research and clinical spheres. Accordingly, there has been a commitment to determine how best to prevent and manage this injury that so commonly affects young people. Despite this increased attention, and proliferation of research, pediatric concussion as a concept has rarely, if ever, been taken up and questioned. That is, little attention has been directed toward understanding what concussion ‘is’, or how young people are regarded in relation to it. As a result, pediatric concussion is understood in decidedly narrow terms, constructed as such by a biomedical way of knowing. Aim: We aim to demonstrate how conceptualizing concussion, and young people, ‘otherwise’, enabled the co-production of a more nuanced and complex understanding of the experience of pediatric concussion from the perspective of young people. Approach: Drawing on an illustrative case example from a critical qualitative arts-based study, we demonstrate how bringing young people into research as ‘knowers’ enabled us to generate much-needed knowledge about concussion in young people. Implications: The critical thinking put forward in this paper suggests a different approach to pediatric concussion, which is shared in the form of implications for clinical and research practice.
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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 | 0.054 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.044 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| 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".