Young carers’ perspectives on navigating the healthcare system and co-designing support for their caring roles: a mixed-methods qualitative study
Bibliographic record
Abstract
OBJECTIVES: Despite young carers (YCs) providing regular and significant care that exceeds what would normally be associated with an adult caregiver, we need to learn more about their experience interacting with the healthcare system. The primary study aims were to (1) describe YC experiences in interacting with the healthcare system and (2) identify types of support YC recognise as potentially helpful to their caring role. DESIGN AND SETTING: A mixed-methods qualitative study was conducted between March 2022 and August 2022, comprising two phases of (1) semi-structured interviews and focus groups with YCs living in the community to confirm and expand earlier research findings, and (2) a co-design workshop informed by a generative research approach. We used findings from the interviews and focus groups to inform the brainstorming process for identifying potential solutions. RESULTS: Eight YCs completed either a focus group or an interview, and four continued the study and participated in the co-design activity with 12 participants. Phase 1 resulted in three overarching themes: (1) navigating the YC role within the healthcare system; (2) being kept out of the loop; and (3) normalising the transition into caregiving. Phase 2 identified two categories: (1) YC-focused supports and (2) raising awareness and building capacity in the healthcare system. CONCLUSION: Study findings revealed the critical role that YCs play when supporting their families during pivotal interactions in the healthcare system. Like their older caregiver counterparts, YCs struggle to navigate, coordinate and advocate for their family members while juggling their needs as they transition from adolescence to adulthood. This study provides important preliminary insights into YCs encountering professionals, which can be used to design and implement national support structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".