The impact of COVID‐19 related isolation on the mental health of Alzheimer’s disease caregivers: Where does communication technology fit in?
Bibliographic record
Abstract
Abstract Background The COVID‐19 lockdown and social isolation protocols implemented to slow the spread of the virus created a unique environment of separation between individuals with Alzheimer’s disease and related dementias (ADRD) and their informal caregivers. The health and wellness of dementia caregivers has been shown to be affected by the challenges of their caregiving role. Yet the inability to fulfill these roles may exude equally detrimental health outcomes. Furthermore, the impact of communication technologies such as smart phone and tablet apps, is not yet fully understood. This study investigated the mental health outcomes of ADRD caregivers in the wake of widespread COVID‐19 related social isolation, and the influence of app use on these outcomes. Method Caregiver perceptions were gathered via a web‐based survey (available in both French and English). Inclusion criteria included: self‐reported status as a dementia caregiver, 18 years of age or older, and ability to read either English or French. Survey data was analyzed via descriptive statistics and specific variables of interested were investigated deeper via principal component analysis and ordinal regression model analysis. Result A total of 84 complete surveys (67 English, 17 French) were collected. Of these, 80% reported that their loved one was isolated due to some form of institutionalization or hospitalization. Furthermore, 87% of respondents reported that they experienced negative mental health outcomes related to either experiencing, or worrying about isolation from their loved one. Using no or only 1 smart device application was significantly associated with increased likelihood of negative mental health outcomes for the caregiver. Conclusion These findings highlight the need for methods of mitigating the negative effects of physical separation in periods of health and safety‐related lockdowns and isolation. Furthermore, the potential alleviating effect of increased technology use was indicated by the increased risk of health concerns with less app use as compared to more app use. Future studies should further investigate the extent to which various smart personal device applications can facilitate care provision at a distance.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".