Exploring the Social Connection of Young Caregivers of People Living with Dementia in Ontario
Bibliographic record
Abstract
Abstract Background Caregiving at a young age can have negative effects on relationships, mental health, and emotional regulation. Notably, dementia caregivers are at higher risk of experiencing social isolation due to their caregiving responsibilities (1‐3). However, there is a lack of knowledge of the experiences of young caregivers of people living with dementia (YCPLWD). Our project is the first to recognize YCPLWD as a unique population and seeks to improve their social connection, which is how individuals connect with each other, and it is comprised of multiple aspects (4) (Figure 1). Our project goals are to: 1. Describe the experiences of social connection among YCPLWD. 2. Identify barriers and facilitators to social connection. Method Our project applies the Toronto Translational Framework (5) (Figure 2), a patient‐centric approach to identify person‐centered needs in healthcare and co‐create innovative solutions. Qualitative data will be collected through 2‐4 focus groups, each with 6‐8 YCPLWD. Participant criteria include English‐speaking post‐secondary students (aged 18‐24 years) who have provided care to a person(s) living with dementia in the past 12 months and are currently living in the Greater Toronto and Hamilton area. Participants will be recruited using both convenience and snowball sampling. Focus groups will be audio‐recorded, transcribed verbatim, then analyzed using Braun & Clarke’s thematic analysis (6) to identify significant themes. Result Focus group discussions will focus on (1) experiences with and the impact of social connection, and (2) facilitators and barriers to social connection. These sessions will enable YCPLWD to co‐create knowledge with their peers and share unique insights about the social experience of caregiving. Moreover, focus group findings will be mapped onto Liougas et al.’s framework (4) to verify how YCPLWD experience and articulate different aspects of social connection, including resources, activities, and subjective feelings. Conclusion This project will recognize YCPLWD as a vulnerable population with unique and specific needs. Given the heterogeneity of dementia caregiving experiences, YCPLWD may face various challenges that can be further understood. Moreover, by exploring barriers and facilitators to social connection, this project will inform the development of novel resources to educate and support YCPLWD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".