Accessing Rehabilitation after Upper Limb Reconstructive Surgery in Cervical Spinal Cord Injury: A Qualitative Study
Bibliographic record
Abstract
Objectives: To investigate the barriers and facilitators to rehabilitation experienced by individuals with cervical SCI after upper limb (UL) reconstructive surgery. Methods: We conducted a prospective cohort study with a follow-up period of up to 24 months. Data collection occurred at two academic and two Veterans Affairs medical centers in the United States. Participants were purposively sampled and included 21 adults with cervical SCI (c-SCI) who had received nerve or tendon transfer surgeries and 15 caregivers. We administered semi-structured interviews about participants' experiences of accessing rehabilitation services after surgery. Results: Four themes emerged from the data: (1) participants encountered greater obstacles in accessing therapy as follow-up time increased; (2) practical challenges (e.g., insurance coverage, opportunity costs, transportation) limited rehabilitation access; (3) individuals with c-SCI and their caregivers desired more information about an overall rehabilitation plan; and (4) external support systems facilitated therapy access. Conclusion: Individuals with c-SCI experience multilevel barriers in accessing rehabilitation care after UL reconstructive surgeries in the United States. This work identifies areas of focus to mitigate these challenges, such as enhancing transparency about the overall rehabilitation process, training providers to work with this population, and developing, testing, and disseminating rehabilitation protocols following UL reconstruction among people with c-SCI.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".