Impact of COVID-19 on patient health and self-care practices: a mixed-methods survey with German patients
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine German patients': (1) self-estimation of the impact of the pandemic on their health and healthcare; and (2) use of digital self-care practices during the pandemic. DESIGN: Cross-sectional mixed-methods survey. SETTING AND PARTICIPANTS: General practice patients from four physicians' offices located in urban and rural areas of Bavaria, Germany, between 21 July 2020 and 17 October 2020. A total of 254 patients participated (55% response rate); 57% (262 of 459) identified as female and participants had an average age of 39.3 years. Patients were eligible to participate if they were 18 years or older and spoke German, and had access to the internet. RESULTS: (1) Healthcare for patients was affected by the pandemic, and the mental health of a small group of respondents was particularly affected. The risk of depression and anxiety disorder was significantly increased in patients with quarantine experience. (2) Self-care practices have increased; more than one-third (39%) of participants indicated that they started a new or additional self-care practice during the pandemic, and about a quarter (23%) of patients who were not previously engaged in self-care practices started new self-care activities for the first time; however, such practices were not necessarily digital. CONCLUSIONS: Further investigation is required to understand the relationship between digital self-care and public health events such as the COVID-19 pandemic, and to develop strategies to alleviate the burden of the quarantine experience for patients.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".