CREATING NORMALCY: FOSTER CARE FOR CHILDREN AND YOUTH WITH DISABILITIES AND MEDICAL FRAGILITY IN GERMANY
Bibliographic record
Abstract
In the area of foster care concerning children and youth with special needs due to disability or medical fragility, there is a paucity of knowledge and research. In Germany, these groups in foster care who have high special needs are an invisible and neglected population at risk. These children and youth are mostly cared for in residential homes; however, some are living in foster families and benefit from a familial setting. The purpose of the study was to understand how foster parents manage their lives with a child or youth who has special needs, and how they meet the challenges that arise. The qualitative research design used the method of narrative inquiry through in-depth interviews, which were conducted in the German state of Saxony-Anhalt with 19 foster parents from 15 families. Within the framework of grounded theory, the author developed a theoretical structure of the strategies foster parents use for coping. Results showed that foster parents dealt with this new and often unpredictable situation by applying one of three patterns of strategies — action-, resource-, or reflection-oriented — based on their personal experiences and worldview. Understanding these behavioral patterns gives administrative and supportive entities like child welfare systems and agencies a unique and tailored approach to recruit, retain, train, and counsel foster families adequately, and to strengthen their well-being and their ability to perform well for themselves and their children and youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".