Early work experiences, social inclusion and transition to adulthood: The voice of care-experienced young adults
Bibliographic record
Abstract
Summary It is widely recognised that young people in out-of-home care are often involved in a complex process of culminating disadvantage and exclusion. Investing in the core ingredients of social inclusion (participation and interpersonal relationships) while still in care can counterbalance ongoing exclusion processes. In this article, we explore how early work experiences (before the age of 18) can play a role in this. Findings A thematic analysis was performed on interview data from 74 young adults in six countries. Several elements promote community participation (gaining financial autonomy, gaining a feeling of independence, and being able to contribute as a worker) and help to develop a sense of belonging (striving for normality and building long-lasting social connections). Early work experiences also contribute to personal growth (building capabilities and shaping the future). Applications This article highlights how early work experiences have the potential to promote social inclusion for out-of-home care-experienced young people and serve as gateway experiences for both educational and work trajectories. Entry into the world of work should not be postponed until the age of 18. Caregivers can play a role in motivating young people to work while still being in care and helping them to find a job. The experiences gained during these early work experiences can also have a place in the care process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".