A descriptive portrait of human assistance required by individuals with brain injury
Bibliographic record
Abstract
BACKGROUND: Human assistance is a counterweight to disabilities for people living with a traumatic brain injury (TBI). However, there is no clear description of the human assistance used by this population in relation with specific life habits (LH). OBJECTIVES: (1) to describe the proportion of LH performed with human assistance; (2) to explore the characteristics of TBI persons with greater needs for human assistance; (3) to clarify the categories of LH for which persons with TBI need human assistance; and (4) to determine the relationship between the human helper and the person with TBI across different residential settings. METHOD: One hundred and thirty-six individuals with moderate or severe TBI were interviewed using the LIFE-H. RESULTS: Human assistance is used to perform one out of three LH. A greater need for human assistance was associated with the number of impairments, motor limitation to the upper limbs, hemiplegia and receiving public insurance. Human assistance was used more often to perform LH pertaining to social roles than those pertaining to daily living. Close relatives were the most frequent providers of human assistance regardless of the residential setting. CONCLUSION: Given the importance of human assistance in TBI, it is essential to support human helpers during and after rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".