Persuasive Design Techniques and App Design Recommendations to Improve Health Workforce Capability in Rural Health Professionals: What Do Users Want and How Does an App Help?
Notice bibliographique
Résumé
BACKGROUND: Health professionals' perceptions of persuasive design techniques for use in technological solutions to improve health workforce capability have not been previously explored. OBJECTIVE: This study aims to explore rural health professionals' perceptions of health workforce capability and persuasive design techniques; and translate these into recommendations for designing a health workforce capability app to increase their impact and usefulness. METHODS: Qualitative interviews with 13 rural health professionals were conducted. Subsequently, 32 persuasive techniques were used as a framework to deductively analyze the data. Persuasive design technique domains were Primary Task Support, Dialog Support, System Credibility Support, Social Support, and Cialdini's Principles of Persuasion. RESULTS: Persuasive design techniques can be applied across the factors that influence health workforce capability including health and personal qualities; competencies and skills; values, attitudes, and motivation; and factors that operate outside of work and at the team, organizational, and labor market levels. The majority of the 32 persuasive design techniques were reflected in the data from the interviews and led to recommendations as to how these could be translated into practice, with the exception of scarcity. Many suggestions and persuasive design techniques link back to the need for tailored and localized solutions such as the need for country-specific-based evidence, the wish for localized communities of practice, learning from other rural health professionals, and referral pathways to other clinicians. Participants identified how persuasive design techniques can optimize the user experience to help meet rural health professionals needs for more efficient systems to improve patient access to care, quality care, and to enable working in interprofessional team-based care. Social inclusion plays a vital role for health professionals, indicating the importance of the Social Support domain of persuasive techniques. Overall, health professionals were open to self-monitoring of their work performance and some professionals used wearables to monitor their health. CONCLUSIONS: Rural health professionals' perceptions of health workforce capability informed which persuasive design techniques can be used to optimize the user experience of an app. These were translated into recommendations for designing a health workforce capability app to increase likelihood of adoption. This study has also contributed to the further validation of the Persuasive Systems Design model through empirically aligning elements of the model to increase persuasive system content and functionality with real-world applied data, in this case the health workforce capability of rural health professionals. Our findings confirm that these techniques can be used to develop a future prototype of an app that may assist health professionals in improving or maintaining their health workforce capability which in turn may increase recruitment and retention in rural areas. Health professionals need to be central during the design phase. Interventions are needed to provide a supportive environment to rural and remote health professionals to increase their rural health workforce capability.
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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 | 0,016 | 0,048 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».