CARE-LEAVERS’ EXPERIENCES OF HOW MANAGED OPPORTUNITIES FOR INDEPENDENCE CONTRIBUTED TO BUILDING RESILIENCE
Bibliographic record
Abstract
Care-leaving literature widely utilizes resilience theory. This is due to an acknowledgment that while young people who grew up in care frequently achieve poorer outcomes during their transition from care to independent adulthood, some do well despite their challenges. Resilience research is also increasingly interested in the factors that promote resilient functioning during the transition out of care. However, research on how to build young people’s resilience while in care is limited. This paper introduces the notion of “managed opportunities for independence” (MOI), which are guided activities for young people that involve them acting independently. We explore the contribution of MOI in building the resilience of young people in care. Nine care-leavers who had disengaged from various residential care centers in South Africa were purposively sampled. The study employed a qualitative approach and a grounded theory design, with semi-structured individual interviews. Findings depict the range of MOI that care-leavers experienced, the ways in which these benefited them since leaving care, the kinds of independence they currently display as a result, and their suggestions for improving MOI. Implications for practice include proceduralizing MOI and making greater use of such opportunities to prepare young people for leaving care.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".