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Record W2133013849 · doi:10.1177/1049732315588759

Elucidating a Goal-Setting Continuum in Brain Injury Rehabilitation

2015· article· en· W2133013849 on OpenAlexafffund
Anne Hunt, Guylaine Le Dorze, Barry Trentham, Helene J. Polatajko, Deirdre Dawson

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of TorontoBaycrest HospitalUniversité de MontréalHolland Bloorview Kids Rehabilitation Hospital
FundersCanadian Institutes of Health Research
KeywordsEmpowermentRehabilitationGoal settingGrounded theoryPsychologyOccupational therapyAcquired brain injuryApplied psychologyPopulationNursingSocial psychologyQualitative researchMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

For individuals with brain injury, active participation in goal setting is associated with better rehabilitation outcomes. However, clinicians report difficulty engaging these clients in goal setting due to perceived or real deficits (e.g., lack of awareness). We conducted a study using grounded theory methods to understand how clinicians from occupational therapy facilitate client engagement and manage challenges inherent in goal setting with this population. Through constant comparative analysis, a goal-setting continuum emerged. At one end of the continuum, therapists embrace client-determined goals and enable clients to decide their own goals. At the other, therapists accept preset organization-determined goals (e.g., "the goal is discharge") and pay little attention to client input. Although all participants aspired to embrace client-determined goal setting, most felt powerless to do so within perceived organizational constraints. Views of advocacy and empowerment help to explain our findings and inform more inclusive practice.

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)0.024
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.023
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0070.020
Scholarly communication0.0090.009
Open science0.0020.010
Research integrity0.0020.005
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 head0.607
GPT teacher head0.726
Teacher spread0.119 · 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 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

Citations22
Published2015
Admission routes2
Has abstractyes

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