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Record W2129155995 · doi:10.3109/02699052.2012.666364

The close relatives of people who have had a traumatic brain injury and their special needs

2012· article· en· W2129155995 on OpenAlexafffundabout
Hélène Lefebvre, Marie‐Josée Levert

Bibliographic record

VenueBrain Injury · 2012
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsGeneral partnershipReflexivityRehabilitationData collectionQualitative researchSample (material)PsychologyHealth careNursingAdaptation (eye)MedicineSociologyPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

PRIMARY OBJECTIVE: This study aims to paint a picture of the needs of people close to individuals with a TBI and the services offered to answer these needs, from the point of view of the individuals with a TBI and health professionals. RESEARCH DESIGN: This study has a qualitative design and a reflexive group was used to collect data. The démarche réflexive d'analyse en partenariat, DRAP (developing reflexive analysis for partnership) was used as a data collection method. The sample comprised Montreal family members (n = 4), Outaouais family members (n = 8), Abitibi family members (n = 7); Montreal care providers (n = 9), Outaouais care providers (n = 11) and Abitibi care providers (n = 9). MAIN OUTCOMES AND RESULTS: The results show that people close to individuals with a TBI need information on the health problem, specifically with regard to the diagnostic, the prognostic, and the factors that influence it, as well as the steps towards rehabilitation, and care and services. The results show that close ones need specific, quality services and continuity of services. CONCLUSION: In conclusion, the pertinence of this study lies in the desire of close ones and health professionals to ease the adaptation process imposed by a TBI, and to promote the well-being of informal caregivers.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.340
Teacher spread0.291 · 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 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

Citations37
Published2012
Admission routes3
Has abstractyes

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