It takes a village to raise a grandchild: Developing communities of support for grand-families on PEI, Canada
Bibliographic record
Abstract
In this practice brief, we outline our research focused on grandparents raising grandchildren in Prince Edward Island (PEI), Canada. Specifically, we highlight a series of collaborative community workshops implemented as part of our research project. Using a participatory action research approach, we worked with grandparents raising grandchildren to co-design and implement the workshops, which we titled It Takes a Village to Raise a Grandchild. These workshops brought together grandparents raising grandchildren and key community members, including clergy, educators, healthcare providers, politicians, social services, child services, and researchers. The goal of these workshops was to build awareness and increase understanding of the issues facing grand-families and to increase cross-collaboration between sectors to optimize grand-family well-being. Central to the workshops was an opportunity to highlight the lived experience of being a grandparent raising grandchildren with a particular focus on resilience needed to lead a grand-family. These workshops provided a catalyst for interdisciplinary sharing and spurred the development of new partnerships and the creation of supportive networks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".