Exploring the lived experiences of Indigenous grandmothers raising grandchildren in the Kinship Care Program of the child welfare system: digital storytelling rooted in an Indigenous research paradigm
Bibliographic record
Abstract
A growing number of Indigenous grandmothers care for their grandchildren in the Indigenous Child and Family Services (CFS) Authorities in Manitoba, Canada. These grandmothers often face complex issues in addition to being economically and socially disadvantaged. This study explored the lived experiences of grandmothers raising their grandchildren within the Kinship Care Program of the Indigenous CFS Authorities. The objectives of the study were: (1) to understand the lived experiences of Indigenous grandmothers raising their grandchildren, (2) to uncover if, how, and to what extent CFS contributes to grandmothers’ health and well-being, and (3) to explore grandmothers’ perspectives on how they might want to be involved in CFS policy and practice changes and in CFS governance. The Two-Eyed Seeing conceptual model embedded in an Indigenous research paradigm, and storytelling as methodology, combined with digital storytelling as method, facilitated the authentic engagement of grandmothers. Recruitment of knowledge holders/Indigenous grandmothers was challenging, with two Indigenous grandmothers participating in the study. Both knowledge holders created a digital story and shared their digital story in a follow up semi-structured interview and in a talking circle, contributing over 80 hours to the study. Two Indigenous key informants volunteered to participate in an individual one-hour semi-structured interview, viewed the digital stories, and participated in the talking circle with 5 to 6 hours of engagement each. The prioritization of Indigenous grandmothers’ situated knowledge and the meaning-making process gave way to surprising and disturbing findings. It became apparent that there was a collective and powerful story in the unspoken stories; the omnipotent fear of the child welfare system, reliving the past and present traumas of colonization, and fear of exploitation and harm was at the forefront, as well as the ongoing disempowerment of Indigenous grandmothers. Notwithstanding, Indigenous grandmothers/participants demonstrated resiliency, empowerment, and hope as they reclaimed Indigenous womanhood. This study highlights the urgent need for the involvement of Indigenous grandmothers at the helm of CFS governance, policy, and practice changes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".