Digital Technology and Social Work: Utilizing Chatbots for Testing and Assessment
Notice bibliographique
Résumé
This dissertation explores the integration of chatbots into social work practice, aiming to advance professional practice and improve service delivery. With the ability to leverage pre-trained large language models (LLMs) for translation and question-answering systems, chatbots present a promising opportunity to assist social workers in conducting psychological testing and intake assessments. This research addresses three central questions: (1) What are social workers’ attitudes toward chatbots providing social work services? (2) Is there any significant difference between using a chatbot and a pen-and-paper test for psychological testing? And, (3) What is the experience of using chatbots for psychological testing and assessment?To answer these questions, three studies were conducted. The first study employed a qualitative research design, conducting semi-structured one-on-one online interviews with 45 social workers from different cities in China. Thematic analysis revealed that chatbots can simplify document-related tasks, enhance information gathering, provide psychological support, and assist with administrative tasks. However, limitations such as a lack of empathy, inability to adapt to individualized needs, language barriers, and inability to provide physical care were also identified. Additionally, participants expressed concerns about ethical risks such as data security, unemployment risk, and social alienation. The second study investigated the equivalence between chatbot-aided and paper-and-pencil depression symptom testing. Eighty-eight participants were recruited and divided into two groups, with each group undergoing both testing methods in a counterbalanced order. The results indicated no significant difference between the outcomes of the chatbot-aided test and the paper-and-pencil test. This suggests that chatbot-based psychological assessments can be a reliable alternative to traditional methods, while also highlighting new influencing factors such as comprehension issues and linguistic challenges. The third study examined the service user experience of psychological assessments by chatbots, drawing on three theoretical frameworks: Technology Acceptance Model (TAM), Expectation-Confirmation Theory (ECT), and Social Penetration Theory (SPT). A total of 140 participants aged 18–57 were recruited and conducted a psychological assessment with a rule-based chatbot. Data collected through semi-structured interviews were analyzed using deductive and inductive coding. The results showed that participants found the chatbot easy to use and effective in reducing social pressure in self-disclosure. However, concerns about functional limitations, such as lack of follow-up support and diagnostic validity, were also expressed. Furthermore, participants’ willingness to use chatbots was influenced by external factors like institutional and public opinion endorsements. Collectively, these studies demonstrate that chatbots have the potential to enhance the efficiency of social work practice and expand service accessibility. However, careful consideration must be given to technological limitations, ethical frameworks, and cultural contexts to ensure that chatbots complement rather than replace the unique contributions of social workers. Future research should continue to explore the applications of chatbots in social work, addressing the identified limitations and ethical concerns to fully realize the benefits of this technology in the field.
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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 | 0,018 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».