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Record W2160690627 · doi:10.5014/ajot.2010.09188

Effectiveness of Rehabilitation in Enhancing Community Integration After Acute Traumatic Brain Injury: A Systematic Review

2010· review· en· W2160690627 on OpenAlexaff
Il Hwan Kim, Angela Colantonio

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2010
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsRehabilitationOccupational therapyTraumatic brain injuryPsychological interventionMedicineIntervention (counseling)Community integrationPhysical therapyPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: We assessed evidence for post-acute traumatic brain injury (TBI) rehabilitation interventions used to enhance community integration (CI) relevant to occupational therapy. METHOD: We conducted a systematic review of intervention studies on TBI rehabilitation from 1990 to 2007. RESULTS: We analyzed and summarized 10 studies that met the inclusion criteria. Of 10 studies, 7 found that post-acute TBI rehabilitation benefits CI; all effective studies involved occupational therapy or involved interventions occupational therapists can do. CONCLUSION: Many CI programs show positive results and should be studied more rigorously. Such promising programs should also be considered when decisions about post-acute TBI rehabilitation services for clients are being made. To further establish that post-acute TBI rehabilitation interventions improve CI, future studies should include intervention strategies based on injury severity, a control group, and longer term follow-up. The role of occupational therapy in these effective programs should be further explored.

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)0.009
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.041
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.095
GPT teacher head0.472
Teacher spread0.377 · 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 designSystematic review
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

Citations59
Published2010
Admission routes1
Has abstractyes

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