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Record W2102101212 · doi:10.1097/htr.0b013e31828f93db

Assessments of Coping After Acquired Brain Injury

2013· review· en· W2102101212 on OpenAlexaff
Gisela Wolters Gregório, Ingrid Brands, Sven Stapert, Frans R.J. Verhey, Caroline van Heugten

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2013
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsBrandon University
Fundersnot available
KeywordsCoping (psychology)ConceptualizationPsychologyClinical psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify measures of coping styles used by patients with acquired brain injury; to evaluate the conceptualization, feasibility, and psychometric properties of the instruments; and to provide guidance for researchers and clinicians in the choice of a suitable instrument. DESIGN: Systematic review. RESULTS: The search identified 47 instruments, of which 14 were selected. The instruments focused on dispositional coping, situation-specific coping, or domain-specific coping. Psychometric properties were scarcely investigated. The COPE stood out in terms of psychometric properties but had low feasibility. The brief COPE, Coping Scale for Adults-short form, and Utrecht Coping List stood out in terms of feasibility, and the available psychometric properties of these instruments were good. Only the Coping With Health Injuries and Problems was used as other report. CONCLUSION: Information on psychometric properties of coping instruments in acquired brain injury is scarcely available and limits the strength of our recommendations. For patients with mild injuries, we cautiously recommend the COPE and for patients with more severe injuries the brief COPE, Coping Scale for Adults-short form, Utrecht Coping List, and Coping With Health Injuries and Problems-other-report. Other instruments may be used to address particular issues such as coping with a specific stressful situation or illness.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.132
GPT teacher head0.486
Teacher spread0.354 · 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 designOther design
Domainnot available
GenreReview

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

Citations20
Published2013
Admission routes1
Has abstractyes

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