The Real Me: Insight into Youths with Physical Disabilities’ TranXition to Adulthood through Digital Images
Bibliographic record
Abstract
AIMS: Youths with physical disabilities experience various obstacles in their transition to adulthood which can contribute to poorer health and socioeconomic outcomes in later life, compared to their non-disabled peers. Transitional care offers these youths the necessary support to overcome such obstacles. The objective of this study was to explore participants' experiential learning in the development of life skills within the transitional care program TranXition, and their perceived contribution of the program to their goal attainment. METHODS: Data were collected using photo-elicitation. Five participants were recruited from the TranXition program to audio-visually record (photographs or videos) their meaningful experiences in the program and to reflect on them during interviews. RESULTS: Participants felt the TranXition program helped them build their self-awareness and self-efficacy, and to feel more confident and skilled, whether at home, in school or in the community. Moreover, they appreciated the program's group cohesion which facilitated learning life skills from others in order to achieve their goals. Finally, results suggest that group interventions, while important, may need to be complemented by individual consultations. CONCLUSIONS: Rehabilitation programs in real-world settings, such as the TranXition program, may be a promising adjunct to traditional transitional care for youths with physical disabilities.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".