Scoping Review of Handwashing and OCD During COVID-19 Concerning Increased Negative Mental Health
Bibliographic record
Abstract
The COVID-19 virus is spread to the respiratory system by minute airborne particles in contrast to large particle fomite transmission. Yet, an often-repeated health directive during the COVID-19 pandemic was to improve handwashing to limit the spread of the virus. Persistent handwashing can aggravate obsessive-compulsive disorder (OCD), diminishing mental health. Given that handwashing is unlikely to control the spread of COVID-19, it is pertinent to determine if the directive for handwashing to eradicate the COVID-19 virus increased the incidence of OCD and, as such, promoted negative mental health. This scoping review of the parameter, “handwashing, mental health, COVID-19, OCD”, conducted during July 2023, searched six relevant databases. The result was that negative mental health related to increased handwashing was evident both for those already diagnosed with OCD and regarding new cases of OCD throughout the duration of the pandemic. The conclusion is that health officials should update details of their health directives as information becomes available during a pandemic and, concerning COVID-19, the directive to concentrate on handwashing should have been relaxed once it was known that spread of the virus by fomite transmission was improbable. This likely would have reduced the incidence of OCD and improved mental health.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".