Direct Support Professionals’ Perspectives on Using Technology to Help Support Adults With Autism Spectrum Disorder: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Documentation is a critical responsibility for direct support professionals (DSPs) who work with adults with autism spectrum disorder (ASD); however, it contributes significantly to their workload. Targeted efforts must be made to mitigate the burden of necessary data collection and documentation, which contributes to high DSP turnover rates and poor job satisfaction. OBJECTIVE: This mixed methods study aimed to explore how technology could assist DSPs who work with adults with ASD and prioritize aspects of technology that would be most useful for future development efforts. METHODS: In the first study, 15 DSPs who worked with adults with ASD participated in 1 of the 3 online focus groups. The topics included daily tasks, factors that would influence the adoption of technology, and how DSPs would like to interact with technologies to provide information about their clients. Responses were thematically analyzed across focus groups and ranked by salience. In the second study, 153 DSPs across the United States rated the usefulness of technology features and data entry methods and provided qualitative responses on their concerns regarding the use of technology for data collection and documentation. Quantitative responses were ranked based on their usefulness across participants, and rank-order correlations were calculated between different work settings and age groups. The qualitative responses were thematically analyzed. RESULTS: In study 1, participants described difficulties with paper-and-pencil data collection, noted benefits and concerns about using technology instead, identified benefits and concerns about particular technology features, and specified work-environment factors that impact data collection. In study 2, participants rated multiple features of technology as useful, with the highest usefulness percentages endorsed for task views (ie, by shift, client, and DSP), logging completed tasks, and setting reminders for specific tasks. Participants also rated most data entry methods (eg, typing on a phone or tablet, typing on a keyboard, and choosing from options on a touch screen) as useful. Rank-order correlations indicated that the usefulness of technology features and data entry methods differed across work settings and age groups. Across both studies, DSPs cited some concerns with technology, such as confidentiality, reliability and accuracy, complexity and efficiency, and data loss from technology failure. CONCLUSIONS: Understanding the challenges faced by DSPs who work with adults with ASD, and their thoughts about using technology to meet those challenges, represents an essential first step toward developing technology solutions that can increase DSPs' effectiveness and job satisfaction. The survey results indicate that technology innovations should incorporate multiple features to account for different needs across DSPs, settings, and age groups. Future research should explore barriers to adopting data collection and documentation tools and elicit input from agency directors, families, and others interested in reviewing data about adults with ASD.
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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 | 0,019 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».