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Record W3092280748 · doi:10.1111/hex.13138

Loss and recovery after concussion: Adolescent patients give voice to their concussion experience

2020· article· en· W3092280748 on OpenAlexaff
Romita Choudhury, Ash T Kolstad, Vishal Prajapati, Gina Samuel, Keith Owen Yeates

Bibliographic record

VenueHealth Expectations · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConcussionGrounded theoryFocus groupPerspective (graphical)Qualitative researchPsychologyCoping (psychology)NarrativeCognitionPsychological resilienceClinical psychologyMedicinePoison controlInjury preventionPsychotherapistPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Most concussion studies have focused on the perspectives and expertise of health-care providers and caregivers. Very little qualitative research has been done, engaging the adolescents who have suffered concussion and continue to experience the consequences in their everyday life. OBJECTIVE: To understand the experiences of recovery from the perspective of adolescent patients of concussion and to present the findings through their voices. METHODS: Two semi-structured focus groups and two narrative interviews were conducted with a small group of 7 adolescents. Grounded theory was used to analyse the data. RESULTS: Participants experience continuing difficulty 1-5 years after treatment with cognitive, emotional, social and mental well-being. The overriding experience among older adolescents (17-20) is a sense of irreversibility of the impact of concussion in all these areas. CONCLUSION: There is a significant gap between the medical determination of recovery and what patients understand as recovery. Adolescents do not feel 'recovered' more than a year after they are clinically assessed as 'good to go'. Systematic follow-up and support from a multi-disciplinary health-care team would strengthen youths' coping and resilience.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.746
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.068
GPT teacher head0.367
Teacher spread0.299 · 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

Citations35
Published2020
Admission routes1
Has abstractyes

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