ALONE, LOST, AND UNPREPARED BY THE SYSTEM: INDIGENOUS CARE LEAVERS’ EXPERIENCES OF AGING OUT OF CHILD WELFARE CARE IN MANITOBA
Bibliographic record
Abstract
Limited research has been carried out on Indigenous young people transitioning out of care in Canada. This article provides a coherent and meaningful account of the journey of aging out of care in the province of Manitoba, as presented by 17 Indigenous care leavers. Using a qualitative multilevel approach grounded in first-person narratives, this study focused on incorporating and elevating the voices of Indigenous care leavers. Participants detailed their experiences of planning for the transition and aging out of care, and described their life post-care. The care leavers expressed that the Child and Family Services-led process of planning for and transitioning out of care was unclear and failed to engage them as partners, and as a result left them ill-prepared for life post-care. Additionally, participants’ experiences serve as evidence of enduring gaps in transition supports, namely in mental health and interpersonal supports. These supports were often unavailable or inaccessible, challenges that were amplified during participants’ transition out of care and in the post-care period. The experiential knowledge from this study contributes valuable information to inform responsive child welfare transition policies and practices that are reflective of the experiences and needs of the population it intends to serve.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".