“Don’t you recognize me…?”: Insights From Social Media Posts On The Impact of Alzheimer’s Disease On Care Partners
Bibliographic record
Abstract
Abstract Background Caring for someone with Alzheimer’s disease (AD) can have a profound impact on care partners’ wellbeing and daily life. We conducted a social media (SM) review to gain insights into how the impact of AD on care partners is described by persons‐living‐with‐AD (PwADs)/care partners/family members. Method Web‐based searches identified SM data from 4 sources: YouTube, Alzheimer’s Association, Alzheimer Society of Canada, and Dementia UK. English‐language SM posts uploaded between May 2011‐May 2021 shared by PwADs/care partners/family members were included in the review if posts discussed the impact of AD on care partners. SM data were analyzed thematically. Result Of 279 SM posts identified, 55 met the review criteria (21 blog posts, 21 videos, 13 comments). The 55 posts were shared by 70 contributors (4 PwADs and 66 PwAD care partners/family members) who discussed self‐reported/observed impacts of AD on care partners and family members. Notable areas affected included psychological and emotional well‐being (n = 53, 75.7%); a profound theme raised was the emotional distress and sadness (n = 24; 34.3%) associated with the care partners’ experience of ‘living bereavement’; i.e., the gradual psychological receding of a loved one prior to their passing. Care partner emotional distress was also exacerbated by the PwAD’s AD‐related symptoms such as altered behaviour and memory loss. Contributors also reported impacts on care partner, overall health‐related‐quality‐of‐life (n = 27, 38.6%), daily life (n = 9, 12.9%), work and employment (n = 8, 11.4%) and physical health (n = 5, 7.1%). Prioritisation of patient care had long‐term consequences for care partners such as diminished personal wellbeing, family and personal sacrifices including loss of employment and unanticipated financial burden. Conclusion SM research provides a unique approach to explore the experiences and challenges associated with caring for someone with AD potentially not captured through traditional research methods. Insights from SM data emphasized the burden of ‘living bereavement’ for care partners and the subsequent need for improved coping strategies/interventions to enable care partners to better manage this phenomenon. The care partners’ candid posts provided valuable insights on the psychological, social, and financial impairments associated with becoming a care partner, which require further investigation.
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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 | 0.012 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".