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Record W7132907189

Social Support and Concussion: Exploring the Experiences of Youth Facing Barriers

2023· dissertation· W7132907189 on OpenAlexaff
Zane Grossinger

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsConcussionSocial supportIdentity (music)Interpretative phenomenological analysisPopulationSocial identity theorySuicide preventionLived experience
DOInot available

Abstract

fetched live from OpenAlex

The purpose of the study was to explore the experiences of youth facing barriers to positive development who have sustained a concussion to develop a detailed and rich understanding about what constitutes meaningful social support during concussion recovery. The lead author, who is a former high-performance athlete with a history of multiple concussions, interviewed four participants within this population and explored their concussion experiences. Using interpretive phenomenological analysis, participants explained how they valued social support that helped them preserve their independence, allowed them to retain their identity following their injury and made them feel less isolated and alone. They also highlighted the importance of adult figures providing dependable support by prioritizing their health. These results may resonate with others who have sustained a concussion that experience similar life circumstances and can be used to help them articulate or indicate the kind of social support they desire to better facilitate recovery.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0100.008
Scholarly communication0.0060.004
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.180
GPT teacher head0.430
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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