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Record W4320032849 · doi:10.1080/09638288.2023.2176553

The influence of facemasks on communication in healthcare settings: a systematic review

2023· review· en· W4320032849 on OpenAlexaboutno aff
Rebecca Francis, Michael O. Leavitt, Colin McLelland, David Hamilton

Bibliographic record

VenueDisability and Rehabilitation · 2023
Typereview
Languageen
FieldMedicine
TopicInfection Control and Ventilation
Canadian institutionsnot available
Fundersnot available
KeywordsSystematic reviewHealth careMedicinePsychologyMEDLINEPolitical science

Abstract

fetched live from OpenAlex

PURPOSE: Although a well-established aspect of healthcare practice, the impact of facemasks on verbal communication is surprisingly ambiguous. MATERIALS AND METHODS: A systematic search was conducted in APA PSYCHinfo, CINAHL, NHS Knowledge Network, Medline and SPORTDiscus databases from inception to November 2022 according to the PRISMA guidelines. Studies reporting an objective measure of speech understanding in adults, where information was transmitted or received whilst wearing a facemask were included. Risk of bias of included studies was assessed with the Newcastle-Ottawa score. RESULTS: Four hundred and thirty-three studies were identified, of which fifteen were suitable for inclusion, incorporating 350 participants with a median age of 49 (range 19 to 74) years. Wide heterogeneity of test parameters and outcome measurement prohibited pooling of data. 93% (14 of 15) studies reported a deleterious effect of facemasks on speech understanding, and 100% (5 of 5) of the included studies reported attenuation of sound with facemask usage. Background noise added further deleterious effects on speech understanding which was particularly problematic within hearing-impaired populations. Risk of bias in included studies varied but overall was modest. CONCLUSIONS: Despite considerable complexity and heterogeneity in outcome measure, 93% (14 of 15) articles suggest respiratory protective equipment negatively affects speech understanding in normal hearing and hearing-impaired adults.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.387
Teacher spread0.354 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations15
Published2023
Admission routes1
Has abstractyes

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