Understanding Young Carers and their Leisure (UYCL): A Critical Participatory Action Research (CPAR) Initiative
Bibliographic record
Abstract
As of 2012, Statistics Canada estimated there were a minimum 1.2 million young Canadians supporting a family member or friend with a long-term health condition, disability, or as an older adult (Statistics Canada, 2012). Young carers voices and perspectives are predominantly missing from representations of their lived experiences in research, social policy, and support services. Leisure may have important implications for supporting young carers in their care roles; however, little attention has been brought to understanding young carers’ meanings and experiences of leisure. \n \nThis critical participatory action research (CPAR) project partnered with young carers and staff supporting them to expand our understandings of young carers' experiences of care and how those care experiences shape leisure. Our team, made up of staff from two young carer organizations in Ontario and four, bright young carers, collaboratively and critically explored dominant conceptualizations of young carers and their leisure to better understand how to support young carers in their care roles. Drawing on critical youth studies and an authentic partnership approach, our CPAR process brings attention to the possibilities of involving young carers in actions and decision-making throughout all phases of the research. \n \nOur CPAR project brought attention to four key themes: There is Nothing Unnatural About Being a Young Carer: It’s About Just Being Human; Tensions in Understandings and Experiences of Young Carers; Leisure as Relational Moments of Rejuvenation in Everyday Life, and; Being Acknowledged as Relational Beings. Through privileging the perspectives of young carers, our findings contribute an alternative conceptualization of young carers and their leisure, filling gaps in research, policy, and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.109 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.039 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.005 | 0.008 |
| 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".