Competency development for a volunteer navigation program to support caregivers of people living with dementia: A modified e-Delphi method
Bibliographic record
Abstract
Caregivers of people living with dementia are pillars of the care community. Providing them with adequate support throughout their caregiving journey is essential to their quality of life and may also contribute to improving the care of people living with dementia. Nav-CARE (Navigation - Connecting, Advocating, Resourcing, Engaging) is a volunteer-led navigation program that provides support to older adults with life-limiting illnesses who are living in the community. However, Nav-CARE does not provide support directly to caregivers of people living with dementia. To adapt Nav-CARE to support caregivers, we needed to establish caregivers' needs and the competencies volunteer navigators should be trained in to support caregivers to meet these needs. To do so, a modified e-Delphi method was utilized, which consisted of administering three sequential questionnaires to a panel of 35 individuals with expertise in a variety of dementia related domains. Through this, two final lists of 46 caregivers' needs and 41 volunteer competencies were established to inform the development of volunteer navigator training curriculum. Findings suggest that trained volunteer navigators may be able to support caregivers of people living with dementia throughout the disease trajectory and can be used to inform the development of future dementia navigation programs.
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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 | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".