Sensory effects of COVID-19 in wine professionals
Bibliographic record
Abstract
OBJECTIVE: To evaluate the sensory impacts of COVID-19 infection among wine professionals, and consequences on personal and professional well-being. Our goal was to better understand these effects on an occupational cohort who relies heavily on intact sensory function. DESIGN: The study employs an explanatory sequential mixed methods design comprising of two distinct phases: 1) a cross-sectional survey, followed by 2) qualitative interviews with a subsample of survey respondents. SETTING: Wine professionals were recruited at the global level, excluding locations where Instagram is restricted or banned. PARTICIPANTS: Wine professionals (n=128) (ages ≥ 19 years) infected with COVID-19 and experienced sensory impacts were included in the study analysis. Eleven participants completed qualitative interviews. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Symptom profiles, details of taste and smell impact on personal and professional well-being. Effects on specific wine tasting attributes were also evaluated. RESULTS: Infected participants reported typical COVID-19 symptoms. The most frequent first noticed symptoms were sore throat (21.09%; 27/128), loss of taste or smell (19.53%; 25/128), fever (17.19%; 22/128) and cough (16.41%; 21/128). For those infected and sensory affected, the extent of taste and smell loss was most reported as severe in the majority of cases. The duration of taste and smell loss was resolved within 4 weeks for most participants. A vast proportion of participants reported an impact on their involvement in the wine profession, with the impact severity ranging from significant (20.31%; 26/128), somewhat (57.03%; 73/128), and not at all (22.66%; 29/128). Additionally, participants predominately reported having some impact on their overall quality of life, which was characterized as severe (7.14%; 9/126), moderate (37.30%; 47/126), mild (30.16%; 38/126), and none (25.40%; 32/126). CONCLUSION: Wine professionals infected with COVID-19 and who experienced sensory alterations reported concerns about their professional and personal well-being with worries about a potential changing life narrative from losing vital sensory attributes. Policies to provide further resources, including therapeutics, for these professionals and others who suffer from sensory dysfunction are warranted.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".