When words fail, pictures speak: A visual autoenthography of a female university student-athlete with post-concussion syndrome
Bibliographic record
Abstract
Sport-related concussions (SRC) are an epidemic among all levels of sport (Noble & Hesdorffer, 2013) and an increasing number of athletes are experiencing prolonged symptoms, known as post-concussion syndrome (PCS). This study used a combination of autethnography and art-based methods to demonstrate the experiences of a female university student-athlete with PCS. Data collected through retrospective personal reflection, personal journals, medical records, and art-work created during the recovery period was analyzed through a 3 step qualitative content analysis (preparation, organization, and reporting) using manifested and latent content (Elo & Kyngas, 2008). The manifested content revealed how I was feeling in the specific context when the art was created. For example, communicating where exactly I felt my headache, or feeling trapped in a dark hole. However, when looking deeper into the latent content, more complex psychological constructs revealed the dissociation between injury and athlete, the loss of identity, and the lack of control in the recovery process. This novel integration of art-based methods in the research of the psychology of sport injury and rehabilitation provides a new perspective on the experiences of athletes with SRC and/or PCS. Sharing my experience with PCS can normalize the negative psychosocial responses to injury and rehabilitation of other athletes, as well as educate rehabilitative professionals about these responses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".