Caregiving for People With Spinal Cord Injury Undergoing Upper Extremity Reconstructive Surgery: A Prospective Exploration of Lived Experiences, Perioperative Care, and Change Across Time
Bibliographic record
Abstract
Background: Nerve transfer (NT) and tendon transfer (TT) surgeries can enhance upper extremity (UE) function and independence in individuals with cervical spinal cord injury (SCI). Caregivers are needed to make this surgery possible, yet caregivers experience their own set of challenges. Objectives: This comparative study explored the perioperative and nonoperative experiences of caregivers of individuals with cervical SCI, focusing on daily life activities, burden, and mental health. Methods: Caregivers of individuals with cervical SCI were recruited and grouped by treatment plan for the person with SCI: (1) no surgery (NS), (2) TT surgery, and (3) NT surgery. Semistructured interviews were conducted at baseline/preoperative, early follow-up/postoperative, and late follow-up/postoperative. Caregivers were asked about their daily life, mental health, and challenges related to caregiving. Interviews were audio recorded, transcribed verbatim, and analyzed using thematic analysis. Quantitative, single-item standardized burden score (0-100) data were collected at each timepoint. Results: Participants included 23 caregivers (18 family members, 4 friends, 1 hired professional). The surgeries often brought hope and motivation for caregivers. Caregivers reported increased burden immediately following surgery (less for the NT compared to TT subgroup) yet no long-term changes in the amount and type of care they provided. NS caregivers discussed social isolation, relationship dysfunction, and everyday challenges. Conclusion: Health care providers should consider the changing needs of SCI caregivers during perioperative rehabilitation. As part of the shared surgical decision-making approach, providers should educate caregivers about the postoperative process and the extent and potential variability of short- and long-term care needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".