Representations of youth concussion “recovery” in health and rehabilitation sciences: a critical conceptual review
Bibliographic record
Abstract
PURPOSE: In the field of youth concussion, the concept of "recovery" has undergone little critical scrutiny. As a result, little is known about what recovery "is" (what recovery looks like, what constitutes a "good" recovery, how youth understand their own recovery). MATERIALS AND METHODS: We conducted a critical conceptual review of 15 texts from health and rehabilitation sciences that implicitly or explicitly conceptualized youth concussion recovery. RESULTS: We identified what we have termed a dominant recovery narrative that was re/produced across texts. We have organized this narrative along three intersecting critical analytic threads: recovery as return to "normal," a timeline to recovery, a responsibility to recover. We elaborate each thread, demonstrating the ways they converged to represent concussion "recovery" in reductive terms (i.e., undergirded by ableism and developmentalism), making it difficult to think of recovery, and recovering youth, in any other terms. The authority of the dominant narrative was seldom questioned across texts. However, there were outliers to our synthesis, which offered rare points of resistance and demonstrated how recovery might be oriented "otherwise" (e.g., as living well with symptoms). CONCLUSION: This study has important implications for rehabilitation practice and research which are discussed.
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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 | 0.047 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 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".