Improving Outcomes for Children in Care: A Collaborative Approach
Bibliographic record
Abstract
Manitoba has the highest rates of children in care throughout all of the child welfare system in Canada. Manitoba’s children in care are one of our most vulnerable populations, who rely on the systems and adults within their lives to provide adequate support to ensure positive life outcomes. Unfortunately, the outcomes for children in care in Manitoba are abysmal. One key statistic that needs immediate attention is the graduation rates of Manitoba’s children in care – only about 1/3 of these children will graduate from high school (Government of Manitoba, 2016a.) School counsellors in Manitoba are teachers with specialized training in mental health and wellbeing and are well-positioned to work in collaboration with child welfare social workers who serve as the guardians to children in care in Manitoba. Using a phenomenological qualitative approach, this study is based on interviews conducted with three Manitoba school counsellors who work with students in Winnipeg, Manitoba. School counsellors were interviewed individually and asked about their experiences and perceptions in relation to working with children in care and in collaboration with CFS social workers. Data analysis explored the experiences and key themes of Manitoba school counsellors working to improve the educational and life outcomes of children in care. Key findings included the necessity of and call for regular, intentional and ongoing communication between Manitoba school counsellors and CFS social workers, the desire for changes to policies and practices between Manitoba school counsellors and CFS social workers, and the overall need to address issues related to the funding and staffing of both Manitoba’s Education and CFS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.054 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".