Evidence map›Paper›PMID 40144292›Full record

ArticleRoyal Society open science2025

Face mask mandates alter major determinants of adherence to protective health behaviours in Australia.

Matthew Ryan, Jinjing Ye, Justin Sexton, Roslyn I Hickson, Emily Brindal

Abstract read
In one paragraph

Article in Royal Society open science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Matthew RyanCommonwealth Scientific and Industrial Research Organisation (CSIRO), Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0003-2373-4384
Jinjing YeSchool of Civil Engineering, University of Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0009-0006-0794-5084
Justin SextonCommonwealth Scientific and Industrial Research Organisation (CSIRO), Adelaide, South Australia, Australia.
Roslyn I HicksonCommonwealth Scientific and Industrial Research Organisation (CSIRO), Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0001-6453-7745
Emily BrindalCommonwealth Scientific and Industrial Research Organisation (CSIRO), Adelaide, South Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Face mask wearing is a protective health behaviour that helps mitigate the spread of infectious diseases such as influenza and COVID-19. Understanding predictors of face mask wearing can help refine public health messaging and policy in future pandemics. Government mandates influence face mask wearing, but how mandates change predictors of face mask wearing has not been explored. We investigate how mandates changed predictors of face mask wearing and general protective behaviours within Australia during the COVID-19 pandemic using cross-sectional survey data. We compared four machine learning models to predict face mask wearing and general protective behaviours before and after mandates started in Australia; ensemble, tree-based models (XGBoost and random forests) performed best. Other than state, common predictors before and after mandates included age, survey week, average number of contacts, wellbeing, and perception of illness threat. Predictors that only appeared in the top ten before mandates included trust in government, and employment status; and after mandates were willingness to isolate. These distinct predictors are possible targets for future public health messaging at different stages of a new pandemic.

Indexed as

face masksmachine learningpolicy mandates

Identifiers

PMID40144292
PMCPMC11938299

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.