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Record W4392125807 · doi:10.3233/wor-230418

Work-related and non-work-related mild traumatic brain injury: Associations with mental health and substance use challenges in a Canadian population-level survey

2024· article· en· W4392125807 on OpenAlexafffundabout
Christine M. Wickens, Robert E. Mann, Gina Stoduto, Danielle Toccalino, Angela Colantonio, Vincy Chan

Bibliographic record

VenueWork · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity Health NetworkToronto Rehabilitation InstitutePublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
FundersOntario Ministry of Health and Long-Term CareWorkplace Safety and Insurance Board
KeywordsMental healthMedicinePopulationOdds ratioLogistic regressionPoison controlPsychiatryOccupational safety and healthInjury preventionTelephone interviewDemographyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Mild traumatic brain injury (mTBI) can profoundly impact overall health, employment, and family life. Incidence of mTBI in the workplace represents an important subgroup with poorer outcomes. Mental health (MH) and substance use (SU) challenges are a primary correlate of TBI, but are rarely assessed among individuals with a work-related (wr)-mTBI, particularly at a population-level. OBJECTIVE: This study aimed to assess the association between lifetime wr-mTBI and non-wr-mTBI and the experience of MH and SU challenges. METHODS: The 2019 Centre for Addiction and Mental Health (CAMH) Monitor is a cross-sectional telephone survey of adults aged≥18 years in Ontario, Canada, employing a stratified (six regions) two-stage (telephone number, respondent) list-assisted random digit dialing probability selection procedure (N = 1792). Adjusting for sociodemographic variables, binary logistic regression was conducted to assess the association between lifetime wr-mTBI and non-wr-mTBI (relative to no TBI) and four outcomes: hazardous use of alcohol and of cannabis, psychological distress, and fair/poor mental health. RESULTS: Adjusting for sociodemographic variables, non-wr-mTBI demonstrated increased odds of hazardous alcohol (AOR = 2.12, 95% CI = 1.41, 3.19) and cannabis use (AOR = 1.61, 95% CI = 1.05, 2.45), psychological distress (AOR = 1.68, 95% CI = 1.14, 2.49), and fair/poor mental health (AOR = 1.70, 95% CI = 1.11, 2.59). Lifetime wr-mTBI demonstrated increased odds of reporting psychological distress (AOR = 3.40, 95% CI = 1.93, 5.97) and fair/poor mental health (AOR = 2.16, 95% CI = 1.12, 4.19) only. CONCLUSIONS: Non-wr-mTBI was associated with both MH and SU, whereas wr-mTBI was associated with MH only. MH outcomes were more strongly associated with wr-mTBI than non-wr-mTBI. Physicians, employers, and insurers need to consider the potential association between wr-mTBI and MH, and provide care accordingly.

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)0.002
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 score0.017
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
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 head0.175
GPT teacher head0.362
Teacher spread0.187 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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