Analyzing Informal Caregiving Expression in Social Media
Bibliographic record
Abstract
Caregiving is the act of providing assistance to an individual unable to perform some daily living activities. Caregiving can be either paid or unpaid. An informal caregiver is an unpaid caregiver to an older, sick, or disabled family member or friend on a daily basis. Informal caregiving is associated with increased physical, mental, and emotional stressors contributing to poor health outcomes, caregiver burnout, and increased risk for institutionalization of the older adult care recipient. Informal caregivers manage their stressors through supportive services such as support groups or respite care, but little is known about how they use social media to share their caregiving experience. No work to our knowledge has investigated caregiver use of Twitter to share the caregiving experience.We collect and analyze tweets related to Alzheimer's and Dementia. We present some insights on sentiment of the tweets, statistics of United States geographical locations of the tweeters, and the relationships of the care recipients. In our analysis we found that the majority of tweet sentiment was negative. Moreover, female care recipients are mentioned at a higher frequency than male care recipients in the tweets.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".