Poster (Technology Innovation) ID 1970531
Bibliographic record
Abstract
Background/Objective Spinal cord injuries (SCI) have a devastating effect on individuals incurring this life-changing event; however, it can also affect those integrally involved in their care. Family caregivers often experience negative outcomes, including high levels of burden, leading to decreased psychological well-being. Cognitive behavioural therapy is the most evidence-based treatment to help people identify and modify thoughts and behaviours contributing to their mental health concerns. However, several resource limitations exist. Guided internet-delivered cognitive behaviour therapy (ICBT) offers an evidence-based and accessible approach to psychosocial service delivery. ICBT improved psychosocial outcomes, including depression among persons with chronic health conditions. However, the efficacy of ICBT has yet to be evaluated among these caregivers. Methods/Design We present the protocol of the Well-being Care Partners Program, a 10-week clinician-guided ICBT program tailored for SCI caregivers to improve their well-being. The program was developed through participatory action research involving seven expert members (i.e., clinicians, people with lived experiences). We aim to recruit 30 participants to pilot this program. Participants will complete measures at baseline, post-intervention, and three months post-intervention. The primary outcome will be feasibility assessed through acceptability and limited efficacy (i.e., depression, anxiety, caregiver burden) as suggested by Bowen’s feasibility framework. Discussion This is one of the first pilot trials to test the feasibility, acceptability, and efficacy of a guided ICBT program for SCI caregivers. ICBT is designed to deliver an evidence-based intervention to overcome face-to-face therapy barriers and reach a wider group of patients, including those who might experience difficulties accessing health care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.939 | 0.800 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".