COMPETENCY DEVELOPMENT FOR A PROGRAM TO SUPPORT CAREGIVERS OF OLDER ADULTS WITH DEMENTIA: A MODIFIED E-DELPHI METHOD
Bibliographic record
Abstract
Abstract Nav-CARE is a program that utilizes volunteer navigators to support older adults with life-limiting illnesses who are living in the community. Currently, Nav-CARE is being adapted and expanded to support family and/or friend caregivers of older adults with dementia. In order to begin the process of adapting this program a modified e-Delphi method was utilized. This method consisted of presenting three sequential questionnaires to an expert panel (n=50) of individuals with knowledge of and/or experience in caregiving, dementia, volunteerism and/or navigation. Consensus was established regarding the importance of various caregivers’ needs and the competencies volunteer navigators require in order to meet these needs. This process resulted in a final list of 46 caregivers' needs and 41 volunteer navigator competencies. Two key findings suggest that there is a crucial need for increased access to respite, which influences the caregivers' ability to benefit from additional supports, and that dementia stigma is prominent, which negatively impacts the caregiving experience. Additional findings highlight the need for increased knowledge of dementia among providers, caregivers, and community members, the importance of establishing strong volunteer navigator-caregiver relationships, and the need to balance the agency of older adults living with dementia and their caregivers. Findings from this study informed the development of training curriculum for volunteer navigators, and will be used to guide the ongoing development of the adapted Nav-CARE program.
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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 | 0.044 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".