Evidence map›Paper›PMID 36039312›Full record

ArticleCanadian public policy. Analyse de politiques2021

COVID-19 Health Precautions: Identifying Demographic and Socio-Economic Disparities and Changes over Time.

Anthony Jehn, Matthew Stackhouse, Anna Zajacova

Abstract read
In one paragraph

Article in Canadian public policy. Analyse de politiques, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anthony JehnDepartment of Sociology, Western University, London, Ontario, Canada.
Matthew StackhouseDepartment of Sociology, Western University, London, Ontario, Canada.
Anna ZajacovaDepartment of Sociology, Western University, London, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent coronavirus disease 2019 (COVID-19) pandemic has required the adoption of precautionary health behaviours to reduce the risk of infection. This study examines adherence, as well as changes in adherence, to four key precautionary behaviours among Canadian adults: wearing face masks, social distancing, hand washing, and avoiding large crowds. Data are drawn from Series 3 and 4 of the nationally representative Canadian Perspectives Survey Series, administered by Statistics Canada in June and July 2020. We calculate overall adherence levels as well as changes over time. Logistic regression models estimate each behaviour as a function of demographic and socio-economic characteristics to identify adherence disparities across population segments. We find a nearly universal increase in precautionary behaviours from June to July in mask wearing (67.3 percent to 83.6 percent), social distancing (82.4 percent to 89.2 percent), and avoiding crowds (84.1 percent to 88.9 percent); no significant change occurred in the frequency of hand washing. We observe significant disparities in adherence to precautionary behaviours, especially for mask wearing, in June; female, older, immigrant, urban, and highly educated adults were significantly more likely to adhere to precautionary behaviours than male, younger, Canadian-born, rural, and low-educated adults. By July 2020, these disparities persisted or were slightly attenuated; women, however, had consistently higher adherence to all behaviours at both time points. These findings have substantial implications for policy and potential public health interventions.

Indexed as

adultsCanadaCOVID-19disparitieshealthprecautions

Identifiers

PMID36039312
PMCPMC9395127

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.