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Record W2016107639 · doi:10.1080/0269905021000038429

Crisis and its assessment after brain injury

2003· article· en· W2016107639 on OpenAlexafffund
J. Davis, Monica Gemeinhardt, Caron Gan, Kelley Anstey, Judith Gargaro

Bibliographic record

VenueBrain Injury · 2003
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsMcMaster UniversityWest Park Healthcare Centre
FundersOntario Neurotrauma Foundation
KeywordsAcquired brain injuryReliability (semiconductor)Measure (data warehouse)PsychologyTest (biology)Traumatic brain injuryClinical psychologyPsychiatryRehabilitationComputer scienceData miningNeuroscience

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: To develop a measure to assess crisis after acquired brain injury (ABI). RESEARCH DESIGN: A triangulated research strategy, using both qualitative and quantitative methods, was employed to develop the crisis measure. METHODS AND PROCEDURES: The measure was developed in two phases. In the first phase, by using focus group methodology, the experience of crisis following brain injury was described. The second phase involved developing the questionnaire items, pilot testing the measure and conducting initial reliability testing. MAIN OUTCOMES AND RESULTS: The six themes derived from the content analysis led to the creation of the measure, with versions for individuals who have an ABI, family members and professionals. Test-re-test reliability results (n = 40) were adequate. CONCLUSIONS: The results suggest that crisis is experienced as precarious homeostasis with individuals with brain injury, varying in intensity over time, subjectively viewed as never really absent.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
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 head0.046
GPT teacher head0.383
Teacher spread0.336 · 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

Citations15
Published2003
Admission routes2
Has abstractyes

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