The Embodiment of Post-Concussion Syndrome: Reflexive Research, Acting Athletes, Managing Medical Professionals, and Moral Trepidation
Bibliographic record
Abstract
This thesis undertakes a phenomenological investigation to explore the embodied experience of the recovery from Mild Traumatic Brain Injury (mTBI) from the perspective of high-level athletes alongside the perspective of managing medical professionals.Semi-structured, one-on-one interviews were conducted with six athletes and five medical professionals to provide a partial actors-first perspective that seeks to begin to fill in the qualitative experience of post-concussion syndrome (PCS), which has been lacking within both the medical and social science literature to date.Supplementing this will be a brief consideration of how the media has driven public discourse on the prevalence of this injury (mTBI) and illness (PCS) in professional and high-level amateur athletics, instigating a moral trepidation crystallizing around the uncertainty of possible long-term health consequences of repeated mTBI.This moral trepidation is experienced most viscerally by the parents of Canadian athletes.Seeking to describe the embodied experience of others is understood to be partial within the phenomenological tradition.I hope to draw from my own experiences with mTBI as a means of providing an experiential bridge of understanding to an illness that is described by medical professionals as being especially ambiguous.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".