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Record W4407764516 · doi:10.1016/j.jhin.2025.02.008

Barriers and facilitators to the use of personal protective equipment in long-term care: a qualitative study

2025· article· en· W4407764516 on OpenAlexafffund
Christian Tsang, Vivian Ewa, John Conly, Myles M. Leslie, Jenine Leal

Bibliographic record

VenueJournal of Hospital Infection · 2025
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsCalgary Laboratory ServicesSouth Health CampusUniversity of CalgaryAlberta Health Services
FundersUniversity of Calgary
KeywordsMedicineQualitative researchPersonal protective equipmentTerm (time)NursingLong-term careMEDLINEMedical emergencyCoronavirus disease 2019 (COVID-19)PathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

BACKGROUND: Long-term care (LTC) residents are vulnerable to invasive infection. Appropriate use and training on personal protective equipment (PPE) is important for protecting residents and healthcare workers (HCWs). Studies on the barriers and facilitators to PPE use are limited in LTC settings. AIM: To characterize HCWs' perceptions of barriers and facilitators to the uptake and appropriate use of PPE in LTC facilities in Calgary, Alberta. METHODS: Semi-structured interviews were conducted with HCWs from April to October 2022. Interview transcripts were analysed deductively to identify themes from the Theoretical Domains Framework. FINDINGS: Seven HCWs were interviewed. Barriers and facilitators fell within six overarching themes including: availability and quality of PPE; knowing how to use PPE; familial obligations; convenience and comfort; sense of professional duty; and social influences and identity. Additional factors such as understaffing and the need for more training sessions were highlighted. Strategies to improve PPE use were identified by HCWs, including the use of PPE champions, regular audits, and constructive feedback. CONCLUSION: Identification of unique barriers and facilitators regarding PPE use by HCWs in LTC will facilitate targeted interventions to improve PPE use in this setting.

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)0.016
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.006
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.374
Teacher spread0.348 · 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 designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractno

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