Impact of the COVID-19 Shutdown on Mental Health in Appalachia by Working Status
Bibliographic record
Abstract
INTRODUCTION: To slow the spread of COVID-19 in the United States, businesses shutdown in Spring 2020. Research has indicated the impact on frontlines workers, yet little is known about the impact on those who were not working outside the home or switched to working remotely. PURPOSE: The purpose of this report is to identify the financial and healthcare issues and mental health impact of the COVID-19 shutdown on Appalachians by worker categories. METHODS: An online survey was administered from May 8 - June 6, 2020 to a convenience sample of previous research participants and shared through social media networks, i.e., snowball sampling. Questions investigated mental health, financial and healthcare issues, and employment status. Survey responses were summarized by percentages overall and by working categories. RESULTS: Of the 751 participants, 276 (42%) resided in Appalachia. After removing 17 who lost their job due to COVID-19, 59 (23%) indicated not working outside the home prior to COVID-19, whereas 111 (43%) and 89 (34%) switched to working remotely and continued working outside the home during the shutdown. Respondents were predominately Caucasian and female. Nearly a quarter of participants had lost or reduced income due to the shutdown. Approximately half missed or postponed healthcare appointments. Mental health impacts were similar across the groups, with half of the respondents reporting anxiety due to COVID-19. IMPLICATIONS: Further research is needed to explore health outcomes associated with missed healthcare appointments during the shutdown. Mental health support may be needed equally by those isolated at home and frontline workers.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".