Exploring the retention factors that contribute to the satisfaction and well-being of foster parents in an Indigenous child and family well-being agency in Northeastern Ontario during the COVID-19 pandemic
Bibliographic record
Abstract
This study aims to better understand foster parent satisfaction and well-being by exploring factors contributing to foster parent retention in an Indigenous Children’s Aid Society in Northeastern Ontario. The research methodology used for the study includes a web-based survey, individual interviews, and traditional talking circles. Utilizing Hanlon et al.’s (2021) five major findings that contribute to foster parent retention, the study examines these findings through the Indigenous medicine wheel quadrants defined as Personal Attributes [and skills] in the East (the starting place), Relationship to Child Welfare System in the South (where relationships reside), Material Resources [financial support and access to services aka ‘practical needs’] in the West (representing knowledge and respect), and Training and Peer Support in the North (a place of spirituality and healing), with the centre of the wheel being Indigenous foster parent satisfaction and well-being. Notably, the study occurred during the COVID-19 pandemic, which increased parental stress and added a layer of complexity to an already complex role of fostering. Foster parents identified physical and cultural isolation as predominant themes during the COVID-19 pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".