Perceptions of Hospital Care for Persons With Dementia During the COVID-19 Pandemic: A Social Media Sentiment Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic led to many hospital service disruptions and strict visitor restrictions that affected care of older adult populations. This study investigates perceptions of hospital care for persons with dementia during the COVID-19 pandemic as shared on Reddit's social media platform. RESEARCH DESIGN AND METHODS: This study combined an Opinion Mining Framework with linguistic processing to conduct a sentiment analysis of word clusters and care-based content in a sample of 1,205 posts shared between February 2020 and March 2023 in Reddit's English-language corpus. Data were classified based on reoccurring contiguous sequences of 2 words from our text sample. RESULTS: Hospital dementia care discourse on Reddit advanced 4 negative sentiment themes: (1) fear of poor medication management, hydration, and hygiene, (2) loss of patient advocacy, (3) precipitation of advance directive discussions, and (4) delayed discharge and loss of nursing home bed. One positive sentiment theme also emerged: gratitude toward hospital staff. DISCUSSION AND IMPLICATIONS: Negative sentiment Reddit posts constituted a larger share of the posts than positive posts regarding hospital care for persons with dementia. People who posted about their experiences shared their concerns about hospital care deficiencies and the importance of including informal caregivers in hospital settings, particularly in the context of a pandemic. Implications exist for dementia training, improved quality of care, advance care planning, and transitions in care policies.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".