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Record W1916772127 · doi:10.3233/nre-2006-21406

Cognitive and emotional consequences of TBI: Intervention strategies for vocational rehabilitation

2007· article· en· W1916772127 on OpenAlexaff
Catherine A. Mateer, Claire S. Sira

Bibliographic record

VenueNeurorehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPsychosocialPsychologyPsychological interventionRehabilitationCognitionCoping (psychology)Traumatic brain injuryCognitive rehabilitation therapyClinical psychologyAnxietyAcquired brain injuryContext (archaeology)Cognitive remediation therapyExecutive functionsPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

The effects of a traumatic brain injury on vocational outcome can be predicted on the basis of several factors. Environmental factors such as a supportive work environment, and person specific factors, including the client's age, premorbid occupation, injury variables, level of awareness, psychosocial adjustment, coping skills, and cognitive deficits have all been found to predict return to work following a traumatic brain injury. Some of these factors are amenable to treatment, and clinicians can impact clients' likelihood of returning to work by intervening in various ways. Through case studies and a literature review on the effectiveness of cognitive rehabilitation interventions, we have outlined specific strategies and recommendations for interventions. Cognitive rehabilitation strategies that address attention, memory and executive deficits can improve clients' abilities to manage workplace tasks and demands. Many clients continue to experience problems with social and emotional adjustment following a brain injury that impact return to work. Cognitive behavioural therapy is well suited for improving coping skills, helping clients to manage cognitive difficulties, and addressing more generalized anxiety and depression in the context of a brain injury.

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)0.005
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 score0.730
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.408
Teacher spread0.339 · 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

Citations93
Published2007
Admission routes1
Has abstractyes

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