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Record W3153655703 · doi:10.1017/brimp.2018.14

2018 ASSBI 41st Annual Conference Abstracts

2018· article· en· W3153655703 on OpenAlexaff

Bibliographic record

VenueBrain Impairment · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of ManitobaCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
Fundersnot available
KeywordsContent (measure theory)PublishingComputer sciencePolitical scienceLawMathematics

Abstract

fetched live from OpenAlex

B ackground and aims: The aim was to review the empirical literature to determine the nature and breadth of research into the working alliance (WA) in acquired brain injury (ABI) rehabilitation. Method: A scoping review was conducted, beginning with a systematic search of relevant databases using key search terms. Studies with a focus on the role of the WA in shaping rehabilitation outcomes, and factors influencing perceptions of the WA were included and key information extracted. Results: A total of 10 quantitative studies met inclusion criteria. In most studies, ratings of the WA were compared with other process variables or outcome measures. The WA was linked to positive activity and participation outcomes, including return to work, school and driving. Client-related factors, such as age, level of education and approach to rehabilitation tasks were associated with client and therapist perceptions of the WA. Conclusions: The WA emerged as a complex process that interacts with many factors and processes at play in the rehabilitation environment. Notwithstanding the limitations of the research base, findings indicate that enhancement of the WA may indeed influence rehabilitation outcomes. Allowing time for the development of the WA, and consideration of factors, such as therapist skill, may support therapists to strengthen their alliances in ABI rehabilitation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.130
GPT teacher head0.436
Teacher spread0.306 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2018
Admission routes1
Has abstractyes

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