Telehealth Transition Assistance Program for Acute Spinal Cord Injury Caregivers: Protocol for a Mixed-Methods, Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: While spinal cord injury (SCI) caregiving can be a rewarding experience, caregivers often experience reduced mental and physical health. OBJECTIVE: This article describes the methodology of a study examining the efficacy of a newly developed telehealth Transition Assistance Program (TAP) for caregivers of individuals with acute SCI. METHODS: A mixed-methods, randomized controlled trial is comparing TAP outcomes to that of a standard-of-care control. The study is recruiting for 48 months and incorporating quantitative outcome measures. RESULTS: This study was funded by the Craig H. Neilsen Foundation in April 2017. It was approved by the institutional review boards at Virginia Commonwealth University and the Hunter Holmes McGuire Veterans Affairs Medical Center that same year. Participant recruitment and data collection began in 2018. CONCLUSIONS: This study is implementing and testing an SCI caregiver intervention unlike any created before, targeting a critical time period that, until now, other SCI caregiver interventions have overlooked. Research personnel intend to disseminate the intervention and study findings through the publication of manuscripts and presentations at conferences. If the current study shows improvements in caregiver or patient well-being, the TAP for SCI caregivers could become part of the standard of care for acute SCI. TRIAL REGISTRATION: ClinicalTrials.gov NCT03244098; https://www.clinicaltrials.gov/ct2/show/NCT03244098. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28256.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.109 | 0.015 |
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".