The Interplay Between Technology Performativity and Health Care Professionals in Hospital Settings: Service Design Approach
Notice bibliographique
Résumé
BACKGROUND: The unexpected outbreak of the COVID-19 pandemic and the preventive measures of physical distancing have further necessitated the application of information and communication technologies (ICTs) to enhance the efficiency of work activities in health care. Although the interplay between human agency and technology performativity is critical to the success or failure of ICTs use in routine practice, it is rarely explored when designing health ICTs for hospital settings within the sub-Saharan Africa context. OBJECTIVE: The objective of this study is to explore how the service delivery quality is being influenced by the technology-enabled activities of health care professionals at points of care using a service design strategy. METHODS: An interpretivist stance was assumed to understand the socially constructed realities of health care professionals at points of care in a hospital setting. A service design strategy was identified as suitable for engaging health care professionals in co-design sessions to collect data. A purposive sampling technique was used to identify the participants. Open-ended questions were administered to gain insights into the work activities of physicians and nurses at points of care. Qualitative (textual) data were analyzed using thematic analysis. Ethical concerns about the safety and privacy of participants' data were addressed as per the university ethics review committee and provincial department of health. RESULTS: The findings show that the attributes of human agency and technology features that drive technology performativity result in an interplay between social concepts and technical features that influence the transformation of human-machine interactions. In addition, the interplay of the double dance of agency model can be divided into 2 successive phases: intermediate and advanced. Intermediate interplay results in the perceived suitability or discomfort of health ICTs as experienced by health care professionals at initial interactions during the execution of work activities. Subsequently, the advanced interplay determines the usefulness and effectiveness of health ICTs in aiding task performance, which ultimately leads to either the satisfaction or dissatisfaction of health care professionals in the completion of their work activities at points of care. CONCLUSIONS: The adopted service design strategy revealed that the interaction moments of the tasks performed by health care professionals during the execution of their work activities at point of care determine the features of health ICTs relevant to work activities. Consequently, the ensuing experience of health care professionals at the completion of their work activities influences the use or discontinuation of health ICTs. Health care professionals consider the value-added benefits from the automation of their work activities to ultimately influence the quality of service delivery. The major knowledge contribution of this study is the awareness drawn to both the intermediate and advanced interplay of human-machine interaction when designing health ICTs.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,009 | 0,017 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».