arXiv subjects

Brian E. Perron

Publications and source records attributed to Brian E. Perron.

8 recordsLinked to original sources

arXiv2026

Validation of a Small Language Model for DSM-5 Substance Category Classification in Child Welfare Records

Background: Recent studies have demonstrated that large language models (LLMs) can perform binary classification tasks on child welfare narratives, detecting the presence or absence of constructs such as substance-related problems, domestic violence, and firearms involvement. Whether smaller, locally deployable models can move beyond binary detection to classify specific substance types from these narratives remains untested. Objective: To validate a locally hosted LLM classifier for identifying specific substance types aligned with DSM-5 categories in child welfare investigation narratives. Methods: A locally hosted 20-billion-parameter LLM classified child maltreatment investigation narratives from a Midwestern U.S. state. Records previously identified as containing substance-related problems were passed to a second classification stage targeting seven DSM-5 substance categories. Expert human review of 900 stratified cases assessed classification precision, recall, and inter-method reliability (Cohen's kappa). Test-retest stability was evaluated using approximately 15,000 independently classified records. Results: Five substance categories achieved almost perfect inter-method agreement (kappa = 0.94-1.00): alcohol, cannabis, opioid, stimulant, and sedative/hypnotic/anxiolytic. Classification precision ranged from 92% to 100% for these categories. Two low-prevalence categories (hallucinogen, inhalant) performed poorly. Test-retest agreement ranged from 92.1% to 99.1% across the seven categories. Conclusions: A small, locally hosted LLM can reliably classify substance types from child welfare administrative text, extending prior work on binary classification to multi-label substance identification.

cs.CL↗

arXiv2026

From Job Postings to Curriculum Decisions: Using AI to Generate Workforce Intelligence for MSW Program Planning

Social work programs lack systematic methods to align curricula with employer expectations, typically relying on advisory input and alumni surveys rather than direct analysis of workforce requirements. This paper presents a case study demonstrating how one MSW program used artificial intelligence tools to generate organizational intelligence from job posting data for curriculum planning. Using a locally deployed language model, we classified over 40,000 job postings for MSW relevance and alignment with eight practice specializations, then extracted skills, therapeutic modalities, and technology competencies. Interpersonal Practice dominated the employment landscape, followed by Children, Youth, and Families. Clinical Assessment and Case Management emerged as cross-cutting competencies. Macro-level specializations showed co-occurrence patterns among partially aligned positions that largely disappeared among positions requiring MSW credentials specifically. Trauma-informed care appeared in management and evaluation roles, reflecting its expansion from clinical modality to organizational framework. The methodology demonstrates a transferable approach that other programs can adapt for strategic planning, and the findings illustrate the type of intelligence such analysis can yield. The patterns identified entered faculty deliberation as one input among many, interpreted by stakeholders with contextual knowledge no dataset can fully capture.

cs.DL↗

arXiv2026

Reasoning Language Models for complex assessments tasks: Evaluating parental cooperation from child protection case reports

Purpose: Reasoning language models (RLMs) have demonstrated significant advances in solving complex reasoning tasks. We examined their potential to assess parental cooperation during CPS interventions using case reports, a case factor characterized by ambiguous and conflicting information. Methods: A four stage workflow comprising (1) case reports collection, (2) reasoning-based assessment of parental cooperation, (3) automated category extraction, and (4) case labeling was developed. The performance of RLMs with different parameter sizes (255B, 32B, 4B) was compared against human validated data. Two expert human reviewers (EHRs) independently classified a weighted random sample of reports. Results: The largest RLM achieved the highest accuracy (89%), outperforming the initial approach (80%). Classification accuracy was higher for mothers (93%) than for fathers (85%), and EHRs exhibited similar differences. Conclusions: RLMs' reasoning can effectively assess complex case factors such as parental cooperation. Lower accuracy in assessing fathers' cooperation supports the argument of a stronger professional focus on mothers in CPS interventions.

cs.CY↗

arXiv2026

Small Models Achieve Large Language Model Performance: Evaluating Reasoning-Enabled AI for Secure Child Welfare Research

Objective: This study develops a systematic benchmarking framework for testing whether language models can accurately identify constructs of interest in child welfare records. The objective is to assess how different model sizes and architectures perform on four validated benchmarks for classifying critical risk factors among child welfare-involved families: domestic violence, firearms, substance-related problems generally, and opioids specifically. Method: We constructed four benchmarks for identifying risk factors in child welfare investigation summaries: domestic violence, substance-related problems, firearms, and opioids (n=500 each). We evaluated seven model sizes (0.6B-32B parameters) in standard and extended reasoning modes, plus a mixture-of-experts variant. Cohen's kappa measured agreement with gold standard classifications established by human experts. Results: The benchmarking revealed a critical finding: bigger models are not better. A small 4B parameter model with extended reasoning proved most effective, outperforming models up to eight times larger. It consistently achieved "substantial" to "almost perfect" agreement across all four benchmark categories. This model achieved "almost perfect" agreement (\k{appa} = 0.93-0.96) on three benchmarks (substance-related problems, firearms, and opioids) and "substantial" agreement (\k{appa} = 0.74) on the most complex task (domestic violence). Small models with extended reasoning rivaled the largest models while being more resource-efficient. Conclusions: Small reasoning-enabled models achieve accuracy levels historically requiring larger architectures, enabling significant time and computational efficiencies. The benchmarking framework provides a method for evidence-based model selection to balance accuracy with practical resource constraints before operational deployment in social work research.

cs.CY↗

arXiv2025

Human services organizations and the responsible integration of AI: Considering ethics and contextualizing risk(s)

This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displacement, environmental impact, model bias, and data laborer exploitation -- vary significantly based on implementation context and specific use cases. They challenge the binary view of AI adoption, demonstrating how different applications present varying levels of risk that can often be effectively managed through careful implementation strategies. The paper highlights promising solutions, such as local large language models, that can facilitate responsible AI integration while addressing common ethical concerns. The authors propose a dimensional risk assessment approach that considers factors like data sensitivity, professional oversight requirements, and potential impact on client wellbeing. They conclude by outlining a path forward that emphasizes empirical evaluation, starting with lower-risk applications and building evidence-based understanding through careful experimentation. This approach enables organizations to maintain high ethical standards while thoughtfully exploring how AI might enhance their capacity to serve clients and communities effectively.

cs.CY↗

arXiv2024

AI and Cultural Context: An Empirical Investigation of Large Language Models' Performance on Chinese Social Work Professional Standards

Objective: This study examines how well leading Chinese and Western large language models understand and apply Chinese social work principles, focusing on their foundational knowledge within a non-Western professional setting. We test whether the cultural context in the developing country influences model reasoning and accuracy. Method: Using a published self-study version of the Chinese National Social Work Examination (160 questions) covering jurisprudence and applied knowledge, we administered three testing conditions to eight cloud-based large language models - four Chinese and four Western. We examined their responses following official guidelines and evaluated their explanations' reasoning quality. Results: Seven models exceeded the 60-point passing threshold in both sections. Chinese models performed better in jurisprudence (median = 77.0 vs. 70.3) but slightly lower in applied knowledge (median = 65.5 vs. 67.0). Both groups showed cultural biases, particularly regarding gender equality and family dynamics. Models demonstrated strong professional terminology knowledge but struggled with culturally specific interventions. Valid reasoning in incorrect answers ranged from 16.4% to 45.0%. Conclusions: While both Chinese and Western models show foundational knowledge of Chinese social work principles, technical language proficiency does not ensure cultural competence. Chinese models demonstrate advantages in regulatory content, yet both Chinese and Western models struggle with culturally nuanced practice scenarios. These findings contribute to informing responsible AI integration into cross-cultural social work practice.

cs.CY↗

arXiv2024

A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work

Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.

cs.CL↗

arXiv2024

Demystifying Application Programming Interfaces (APIs): Unlocking the Power of Large Language Models and Other Web-based AI Services in Social Work Research

Application Programming Interfaces (APIs) are essential tools for social work researchers aiming to harness advanced technologies like Large Language Models (LLMs) and other AI services. This paper demystifies APIs and illustrates how they can enhance research methodologies. It provides an overview of API functionality and integration into research workflows, addressing common barriers for those without programming experience. The paper offers a technical breakdown of code and procedures for using APIs, focusing on connecting to LLMs and leveraging them to facilitate API connections. Practical code examples demonstrate how LLMs can generate API code for accessing specialized services, such as extracting data from unstructured text. Emphasizing data security, privacy considerations, and ethical concerns, the paper highlights the importance of careful data handling when using APIs. By equipping researchers with these tools and knowledge, the paper aims to expand the impact of social work research through the effective incorporation of AI technologies.

cs.SE↗