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Record W4385186017 · doi:10.1089/neu.2023.0014

Dynamic Relations Between Psychological Resilience and Post-Concussion Symptoms in Children With Mild Traumatic Brain Injury Versus Orthopedic Injury: An A-CAP Study

2023· article· en· W4385186017 on OpenAlexafffundabout
Brian L. Brooks, Brae Anne McArthur, Miriam H. Beauchamp, William Craig, Quynh Doan, Roger Zemek, Keith Owen Yeates

Bibliographic record

VenueJournal of Neurotrauma · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsHotchkiss Brain InstituteBC Children's HospitalUniversity of British ColumbiaStollery Children's HospitalCentre Hospitalier Universitaire Sainte-JustineAlberta Children's HospitalChildren's Hospital of Eastern OntarioUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsConcussionTraumatic brain injuryOrthopedic surgeryPhysical medicine and rehabilitationInjury preventionMedicinePoison controlPsychologyPhysical therapyHuman factors and ergonomicsResilience (materials science)Occupational safety and healthClinical psychologyMedical emergencyPsychiatryPathology

Abstract

fetched live from OpenAlex

Higher psychological resilience is correlated with less severe post-concussion symptoms (PCS) after mild traumatic brain injury (mTBI) in children, but the directional nature of this relationship remains uncertain. Although traditionally regarded as a stable, trait-like construct, resilience may be malleable and potentially influenced by mTBI and post-concussive symptoms. The current study sought to examine the stability of resilience, elucidate the dynamic nature of the resilience-PCS relation, and determine whether resilience-symptom associations are specific to mTBI or applicable to traumatic injury in general. Participants were children aged 8–16.99 years with either mTBI ( n = 633) or orthopedic injury (OI; n = 334) recruited to participate in a prospective cohort study after presenting acutely to five Canadian pediatric emergency departments (EDs). Symptoms and psychological resilience were assessed at 1 week, 3 months, and 6 months post-injury. Group differences in resilience over time were examined using a mixed linear model, and associations between resilience and symptoms over time were examined using random intercepts cross-lagged panel modeling (RI-CLPM). The mTBI group reported significantly lower resilience than the OI group, but the difference was significantly larger 1 week post-injury ( d = 0.50) than at 3 months ( d = 0.08) and 6 months ( d = 0.10). Cross-lagged panel models indicated that resilience had both stable and dynamic aspects, and both affected and was affected by PCS, although their association varied by time post-injury, symptom measure, and reporter (parent vs. child). Higher parent-reported cognitive symptom severity at 1 week was significantly associated with higher resilience at 3 months ( β = 0.23, p = 0.001). Higher resilience at 3 months was associated with lower levels of parent-reported somatic symptom severity ( β = −0.14, p = 0.004) and fewer total symptoms ( β = −0.135, p = 0.029) at 6 months. Higher resilience at 3 months was associated with fewer child-reported symptoms at 6 months ( β = −0.11, p = 0.030) and, reciprocally, fewer child-reported symptoms at 3 months were associated with higher resilience at 6 months ( β = −0.22, p = 0.001). Notably, injury group was not a significant moderator in cross-lagged models, suggesting that resilience-symptom associations are not specific to mTBI. Psychological resilience and symptoms have bidirectional relationships after injury. Interventions designed to foster resilience have the potential to promote recovery after mTBI specifically and injury more generally.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.441
Teacher spread0.371 · 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 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

Citations6
Published2023
Admission routes3
Has abstractyes

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