Health Service Accessibility, Mental Health, and Changes in Behavior during the COVID-19 Pandemic: A Qualitative Study of Older Adults
Bibliographic record
Abstract
The COVID-19 pandemic has affected the access of older adults to health services. The two objectives of this study are understanding the influence of the COVID-19 pandemic on older adults' access to health services and exploring how health service accessibility during the pandemic influenced older adults' mental health and self-reported changes in behavior. This study included 346 older adults. Content analysis produced five themes: (1) decreased physical accessibility to health care providers (78%); (2) increased use of online health services and other virtual health care (69%); (3) growth in the online prescription of medication (67%); (4) difficulty obtaining information and accessing non-communicable disease and mental health indicators (65%); and (5) postponement of medical specialist consultations (51%). Regarding mental health, three themes emerged: (1) increased symptoms of anxiety, distress, and depression (89%); (2) the experience of traumatic situations (61%); and (3) the augmented use of alcohol or drugs (56%). Finally, the following changes in behavior were indicated: (1) frustrated behavior (92%); (2) emotional explosions (79%); and (3) changes in sleeping and eating behaviors (43%). Access to health services may have influenced the mental health and behavior of older adults, hence interventions in a pandemic must address their interactions with health services, their needs, and their well-being.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".