Don’t Forget the Caregivers! A Discrete Choice Experiment Examining Caregiver Views of Integrated Youth Services
Bibliographic record
Abstract
BACKGROUND: The design and implementation of community-based integrated youth service hubs (IYSHs) is burgeoning around the world. This collaborative model of care aims to address barriers in youth service access by designing services that meet the needs of youth and caregivers. However, heterogeneity across models requires a better understanding of the preferences for key service characteristics. METHOD: A discrete choice experiment was conducted among 274 caregivers of youth aged 14-29 years with mental health challenges. The experiment consisted of 12 attributes with four levels each, representing different service components; additional measures were collected, including demographics and burden assessments. Utility values were calculated, representing the degree of preference for a given level of an attribute. Latent class analysis was conducted to understand subgroups with different service preferences, identifying three latent classes with differing IYSH service preferences. RESULTS: The largest class (n = 173, 63.1%), entitled 'Comprehensive, Integrative Service Access', strongly valued practical aspects of service design, such as rapid access and support for a wide range of needs. The 'Service Process Features' class (n = 67, 24.5%) expressed a relative prioritization of process features of service access, while the smaller 'Caregiver Involvement' (n = 34, 12.4%) class most highly prioritized caregiver involvement in their youths' services. Similar demographic characteristics and caregiver burden were found across classes, although participants in the Caregiver Involvement latent class were supporting younger youth. DISCUSSION AND CONCLUSIONS: Caregivers have diverse youth service preferences and relative priorities that should be taken into account when designing services. System designers and service providers are encouraged to take caregivers' preferences and priorities into account, alongside youth priorities, whether designing service delivery models or an individual service plan for a youth.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".