Concerns of older adults during second COVID wave in India
Bibliographic record
Abstract
Abstract Background A second wave of COVID infections in India, lockdown, restriction on services, and strain on an unprepared health system has had significant impact on older adults. It is important to examine impact of the lockdown on the elderly and their caregivers, useful coping strategies and requirements for support going forwards. Method As part of an ongoing community engagement initiative with older adults, an online survey was conducted in 2020 during the first wave of the pandemic investigating mental health and wellbeing of older adults. Participants who shared details were contacted one year later to understand impact of the second COIVD wave on their wellbeing and coping. An in‐depth telephonic interview was conducted and General Health Questionnaire 5 item were used for data collection. Audio consent was taken prior to interviews which were recorded and transcribed for thematic analysis. Result Out of 58 participants who shared contact details, 16 % (N=9) agreed to participate in the interview. about 40 % refused mentioning they were unwell/ too busy/or agreed to speak at a later date. One female and 8 males participated in the interview between the ages of 36 to 83 years. All participants held post graduate/ professional degrees. 33 % (N=3) participants obtained a GHQ score of 2 and above on the five item General Health Questionnaire indicating presence of depressive features. As compared to the first lockdown, older adults found the second lockdown more stressful due to deaths of close friends and family members. Increased fears about family members with comorbidities contracting the virus, difficulties managing emotional wellbeing, and challenges helping younger family members manage their routines and follow basic COVID protocols were key concerns. Coping strategies found useful were remaining active and engaged at home, attending online mental wellness sessions, practicing yoga/ meditation and spending time with friends/family. Older adults highlighted a need for more emotional support through creation of social media networks and online platforms. Conclusion Preliminary findings from this study emphasize a need for leveraging technology to support emotional wellbeing of older adults during lockdown and COVID restrictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".