The influence of facemasks on communication in healthcare settings: a systematic review
Bibliographic record
Abstract
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. � IMPLICATIONS FOR REHABILITATION� As a result of the covid-19 pandemic, facemask use is now commonplace across all healthcare and rehabilitation settings and has material implications for interpersonal communication.� This systematic review of human communicative studies highlights that the use of facemasks does indeed inhibit communication through effects on speech intelligibility and through sound attenuation.� These effects are evident in both normal hearing and hearing-impaired adults due to the visual cues required with lipreading and facial expressions during communication.� The presence of background noise also produces deleterious effects on speech understanding and is more problematic for hearing-impaired populations.� Simple recommendations to reduce background noise (where possible), to step closer (where socialdistancing rules permit), to speak louder or to use speech to text applications (if practical) could all mitigate these communicative barriers.Further an awareness of persons with hearing impairments, the function (or otherwise) of hearing aids in those patients that require these, and an ability to use transparent facemasks can be specifically helpful.
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 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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".