Peer Mentoring Program for Informal Caregivers of Homebound Individuals With Advanced Parkinson Disease (Share the Care): Protocol for a Single-Center, Crossover Pilot Study
Bibliographic record
Abstract
BACKGROUND: Homebound individuals with advanced Parkinson disease (PD) require intensive caregiving, the majority of which is provided by informal, family caregivers. PD caregiver strain is an independent risk factor for institutionalization. There are currently no effective interventions to support advanced PD caregivers. Studies in other neurologic disorders, however, have demonstrated the potential for peer mentoring interventions to improve caregiver outcomes. In the context of an ongoing trial of interdisciplinary home visits, we designed and piloted a nested trial of caregiver peer mentoring for informal caregivers of individuals with advanced PD. OBJECTIVE: The aim of this study was to test the feasibility of peer mentoring for caregivers of homebound individuals with advanced PD and to evaluate its effects on anxiety, depression, and caregiver strain. METHODS: This was a single-center, 16-week pilot study of caregiver peer mentoring nested within a year-long controlled trial of interdisciplinary home visits. We recruited 34 experienced former or current family caregivers who completed structured mentor training. Caregivers enrolled in the larger interdisciplinary home visit trial consented to receive 16 weeks of weekly, one-to-one peer mentoring calls with a trained peer mentor. Weekly calls were guided by a curriculum on advanced PD management and caregiver support. Fidelity to and satisfaction with the intervention were gathered via biweekly study diaries. Anxiety, depression, and caregiver strain were measured pre- and postmentoring intervention at home visits 2 and 3. RESULTS: Enrollment and peer-mentor training began in 2018, and 65 caregivers enrolled in the overarching trial. The majority of mentors and mentees were White, female spouses or partners of individuals with PD; mentors had a mean of 8.7 (SD 6.4) years of caregiving experience, and 33 mentors were matched with at least 1 mentee. CONCLUSIONS: This is the first study of caregiver peer mentoring in PD and may establish an adaptable and sustainable model for disease-specific caregiver interventions in PD and other neurodegenerative diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT03189459; http://clinicaltrials.gov/ct2/show/NCT03189459. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/34750.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".