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Enregistrement W4390743616 · doi:10.2196/47308

Office Workers’ Views About the Uses, Concerns, and Acceptance of Hand Hygiene Data Collected From Smart Sanitizers: Exploratory Qualitative Interview Study

2024· article· en· W4390743616 sur OpenAlexvenueno aff
Sophie Rutter, Sally Sanger, Andrew Madden, Sukaina Ehdeed, Catherine Stones

Notice bibliographique

RevueJMIR Formative Research · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueInfection Control in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesEconomic and Social Research Council
Mots-clésHand sanitizerHygienePersonal protective equipmentExploratory researchQualitative researchBusinessMedicineEnvironmental healthInternet privacyPublic relationsMarketingCoronavirus disease 2019 (COVID-19)Computer scienceInfectious disease (medical specialty)DiseasePolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: COVID-19 and the prospect of future pandemics have emphasized the need to reduce disease transmission in workplaces. Despite the well-established link between good hand hygiene (HH) and employee health, HH in nonclinical workplaces has received little attention. Smart sanitizers have been deployed in clinical settings to motivate and enforce HH. This study is part of a large project that explores the potential of smart sanitizers in office settings. OBJECTIVE: Our previous study found that for office workers to accept the deployment of smart sanitizers, they would need to find the data generated as useful and actionable. The objectives of this study were to identify (1) the potential uses and actions that could be taken from HH data collected by smart sanitizers (2) the concerns of office workers for the identified uses and actions and (3) the circumstances in which office workers accept HH monitoring. METHODS: An interview study was conducted with 18 office workers from various professions. Interview questions were developed using a framework from personal informatics. Transcripts were analyzed thematically. RESULTS: A wide range of uses of smart sanitizer data was identified including managing hygiene resources and workflows, finding operating sanitizers, communicating the (high) standard of organizational hygiene, promoting and enforcing organizational hygiene policies, improving workers' own hygiene practices, executing more effective interventions, and identifying the causes of outbreaks. However, hygiene is mostly considered as a private matter, and it is also possible that no action would be taken. Office workers were also concerned about bullying, coercion, and use of hygiene data for unintended purposes. They were also worried that the data could be inaccurate or incomplete, leading to misrepresentation of hygiene practices. Office workers suggested that they would be more likely to accept monitoring in situations where hygiene is considered important, when there are clear benefits to data collection, if their privacy is respected, if they have some control over how their data are collected, and if the ways in which the data will be used are clearly communicated. CONCLUSIONS: Smart sanitizers could have a valuable role in improving hygiene practices in offices and reducing disease transmission. Many actionable uses for data collected from smart systems were identified. However, office workers consider HH as a personal matter, and acceptance of smart systems is likely to be dynamic and will depend on the broad situation. Except when there are disease outbreaks, smart systems may need to be restricted to uses that do not require the sharing of personal data. Should organizations wish to implement smart sanitizers in offices, it would be advisable to consult widely with staff and develop systems that are customizable and personalizable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,117
Score d'incertitude au seuil0,566

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,421
Tête enseignante GPT0,545
Écart entre enseignants0,125 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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