Developing competencies for volunteer navigators to support caregivers of children living with medical complexity: a mixed-method e-Delphi study
Bibliographic record
Abstract
Background: Providing specially trained volunteer navigators is one promising strategy for supporting the increasing number of family caregivers who are caring for children living with medical complexity. Objective: The objective of this study was to develop consensus on the role and competencies required for volunteer navigators who support caregivers of children living with medical complexity. Design: This was a mixed-method study using modified e-Delphi and focus group methods. In phase 1, a modified e-Delphi survey with 20 family caregivers and a focus group with 4 family caregivers were conducted to develop consensus on their unmet needs and the potential roles of a volunteer to meet those needs. In phase 2, a modified e-Delphi survey was conducted with experts to develop consensus on the volunteer competencies required to meet the roles identified by family caregivers in phase 1. Results: Findings from phase 1 resulted in 36 need-related items over 8 domains: communication, daily life and chores, emotional support, information and knowledge, respite, support with decision-making, and sharing the caregiving experience. Concerns about the volunteer role included the potential lack of commitment in the absence of remuneration, the complexity of the child's condition that was beyond the role of a volunteer, and a preference for support from individuals they knew. Findings from the phase 2 Delphi survey with professionals resulted in 22 competencies, derived from the roles identified in phase 1, that would be required of volunteers who wished to support these family caregivers. Conclusion: This study provides insight into a role for volunteers in meeting the needs of family caregivers of children living with medical complexity. A volunteer with lived experience and adequate preparation can assist with meeting some of these important needs. Further research is required to better understand the feasibility and acceptability of such a role.
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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 | 0.053 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".