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Record W4399194310 · doi:10.2196/48434

The Impact of COVID-19 Health Measures on Adults With Multiple Chemical Sensitivity: Cross-Sectional Study

2024· article· en· W4399194310 on OpenAlexaffvenue
Riina Bray, Yifan Wang, Nikolas Argiropoulos, Stéphanie Robins, John Molot, Marie-Andrée Pigeon, Michel Gaudet, Pierre Auger, Émilie Bélanger, Rohini Peris

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of OttawaGDG EnvironnementWomen's College Hospital
Fundersnot available
KeywordsPreprintCoronavirus disease 2019 (COVID-19)Cross-sectional studySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakEnvironmental healthMedicinePsychologyVirologyPhysicsInternal medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Multiple chemical sensitivity (MCS) develops in response to repeated small-level chemical exposures or a major exposure in a subset of people, who then experience symptoms that can range from mild to debilitating when exposed to chemicals. The arrival of the COVID-19 pandemic and the stringent health measures put in place may have increased the burden for those living with MCS, as it became more challenging to avoid chemicals that trigger their condition. OBJECTIVE: This study aimed to better understand the lived experience of Canadians living with MCS during the first year of the COVID-19 pandemic. METHODS: An online questionnaire was created to ask participants to compare daily living during the pandemic to before March 11, 2020. Data were collected in January and February 2021. Three areas were investigated: (1) environmental exposures to chemical triggers from ambient air (pollution from industry, farming, and traffic) and indoor air (the smell of cleaning products, cooking odors, and smoke); (2) access to, and satisfaction with, health care visits; and (3) how people experiencing MCS rated contact with their social network. RESULTS: In all, 119 Canadians who had lived with MCS for more than a year completed the questionnaire. The participant sample was mostly female (86.6%, n=103) and highly educated, with 57.1% (n=68) having a university degree. Slightly more than half (57.1%, n=68) were older than 55 years. McNemar chi-square and Wilcoxon signed rank tests were used to evaluate if there were statistically significant changes before ("prepandemic period") and after ("postpandemic period") March 11, 2020. Perceived exposure to pollution from a highway or a road was significantly decreased from the prepandemic to postpandemic period (z=-3.347; P<.001). Analysis of industry or power plants also suggested a significant decrease in the perceived exposure from the prepandemic to postpandemic period (z=-2.152; P=.04). Participants reported an increase in exposure to odors from disinfectants or sanitizers that entered their living environment (P<.001). There was a significant decrease between prepandemic and postpandemic levels of satisfaction when attending in-person meetings with a physician (z=-2.048; P=.04), yet there were no significant differences between prepandemic and postpandemic levels of satisfaction for online or telephone meetings with a physician. Although people with MCS experienced increased social isolation (P<.001), they also reported an increase in understanding from family (P=.03) and a decrease in stigma for wearing personal protective equipment (P<.001). CONCLUSIONS: During the first year of the COVID-19 pandemic, people with MCS were impacted by inaccessibility, loss of social support, and barriers to accessing health care. This study highlights unique challenges and possible benefits associated with the COVID-19 pandemic public health measures for individuals living with MCS. These findings can guide decision makers to improve policies on accessibility through appropriate accommodation measures.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.089
GPT teacher head0.480
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes2
Has abstractyes

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