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Record W2808599529 · doi:10.1080/09638288.2018.1474495

Traumatic brain injury resiliency model: a conceptual model to guide rehabilitation research and practice

2018· article· en· W2808599529 on OpenAlexafffund
Emily Nalder, Laura R. Hartman, Anne Hunt, Gillian King

Bibliographic record

VenueDisability and Rehabilitation · 2018
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalMarch of Dimes CanadaToronto Rehabilitation InstituteUniversity of Toronto
FundersSocial Sciences and Humanities Research Council
KeywordsPsychologyRehabilitationPsychological resilienceTraumatic brain injuryContext (archaeology)Coping (psychology)Acquired brain injuryPsychological interventionApplied psychologyClinical psychologyPsychotherapist

Abstract

fetched live from OpenAlex

There is a growing trend in traumatic brain injury (TBI) rehabilitation, and research, to focus on the processes of adaptation following the injury. Resiliency is an umbrella term describing the range of personal protective factors, environmental supports and resources, as well as self-regulatory processes, engaged in response to adversity. An affective, cognitive, and behavioural self-regulatory process model of resiliency in the workplace was adapted to suit the TBI context. Through a narrative review of the literature pertaining to brain injury rehabilitation, participation, and resilience, we substantiated the model, and explained how resiliency can frame research on life experiences following the injury. TBI represents a cascading adversity as the injury and subsequent life experiences (e.g., job loss) shape adaptation. Resiliency is shaped by: personal characteristics (e.g., hope, social functioning, self-awareness, memory, spirituality, coping, and self-efficacy), environmental resources/supports (e.g., services and social support), and self-regulatory processes that lead to the resiliency-related outcomes, which we suggest involve re-engaging in activities, adapting participation, and reconstructing identity. This conceptual model outlines and defines the factors and processes operating and contributing to resiliency following TBI. Recommendations for future research are outlined. Implications for rehabilitation Investigating resiliency processes can move the traumatic brain injury field beyond examining individual traits and protective factors, to transactional processes that influence participation experiences and opportunities over time. The Traumatic Brain Injury Resiliency Model can be used to frame the targets and desired outcomes of rehabilitation interventions, such as self-regulatory processes or environmental supports known to enhance resiliency. Studying resiliency will help to shift the paradigms of traumatic brain injury research, and rehabilitation practice, to a focus on life experiences and adaptation, helping individuals, clinicians, and families consider processes of positive change, rather than focusing solely on adversity.

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)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.006
Scholarly communication0.0000.001
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 head0.113
GPT teacher head0.520
Teacher spread0.407 · 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.

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

Citations41
Published2018
Admission routes2
Has abstractyes

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