Stepping Up and Stepping In: Exploring the Role of Nurses in Supporting Grandparents Raising Grandchildren
Bibliographic record
Abstract
This study focused on the experiences of grandparents raising grandchildren in rural, Prince Edward Island, Canada. Termed grand-families, there are numerous reasons why grandparents must step up and step in to care for their grandchildren. Often these reasons are related to their adult children's struggles with mental illness and substance use disorders. Adopting Clandinin and Connelly's approach to narrative inquiry, we present findings from the conversational interviews conducted with 12 grandparents raising their grandchildren. Interview data were analyzed through the narrative dimensions of time, place, and relationship. Findings are presented as rich narratives which illuminate the evolution and storied experiences of grand-families. Particularly revealing are the challenges grandparents face as they navigate various systems, including health care, that do not acknowledge the uniqueness of their family form. Nurses work with grand-families across varied clinical settings. Grounded within the philosophy of Patient and Family Centered Care and family nursing theory, this article offers recommendations for supportive interventions that nurses can implement when caring for grand-families across clinical settings. This study has the potential to facilitate the development of evidence-based supports and services, which are responsive to the needs, realities, and complexities of grand-families.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".