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Record W4414379089 · doi:10.1080/09602011.2025.2558885

Exploring the interactions of athletes and their social support network following sport-related concussion

2025· article· en· W4414379089 on OpenAlexaff
Alexandra Repper, Makine Boukhari, Lorelie Roderbourg, Jeffrey G. Caron

Bibliographic record

VenueNeuropsychological Rehabilitation · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsSocial supportTimelineAthletesThematic analysisConcussionPerspective (graphical)Social network (sociolinguistics)

Abstract

fetched live from OpenAlex

Research on social support following sport-related concussion (SRC) has largely been examined from the athlete perspective. This qualitative study explored social support interactions during recovery following SRC. We conducted semi-structured interviews with six athletes and 16 individuals who were identified as being part of the athletes' social support network (e.g., teammates, friends, or family members). All 22 participants in this study completed a timeline mapping activity, which allowed participants to share details about the athletes' SRC recovery, including the type and timing of support provided and received. Using thematic analysis, we found three themes. First, we found that social support was optimal when perceptions of social support were aligned (e.g., delivery and perceived impact on recovery). Second, we found several instances where challenges arose in the social support relationships, often stemming from incongruent perspectives (e.g., expectations and perceptions of support differed). Third, members of the support network described some of the barriers they faced when attempting to provide social support to athletes. Overall, these results add to the literature by demonstrating the good (aligned perspectives), the bad (incoherent perspectives), and the challenges with the social support relationships following SRC from the perspective of athletes and members of their support network.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.247

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.381
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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