DOI: 10.6084/m9.figshare.31235962
title: Supplementary materials for: "Trust is the First Algorithm": A Socio-Technical Framework for Generative AI Integration in a High-Stakes, Resource-Constrained Context

description: <b>Research Overview</b>This repository contains the complete supplementary materials for a groundbreaking qualitative study that develops a novel socio-technical framework for understanding trust in generative artificial intelligence (GenAI) within high-stakes, resource-constrained healthcare environments. Using Palestinian nursing as a critical case study, this research reveals how trust functions not as a passive outcome but as an active, foundational precondition that must be deliberately constructed before AI integration can succeed.<b>Study Design and Methodology</b><b>Research Design:</b> Qualitative descriptive study employing a critical case study approach<b>Sample:</b> 25 registered nurses purposively sampled from diverse Palestinian healthcare settings (public hospitals, private clinics, primary care centers)<b>Data Collection:</b> Semi-structured interviews conducted in Arabic (May-June 2024), each lasting 45-75 minutes<b>Analysis:</b> Reflexive thematic analysis (Braun & Clarke, 2006, 2019) conducted using NVivo 12<b>Ethical Approval:</b> Institutional Review Board of Nablus University (Reference: Nrs. May 2024/7)<b>Theoretical Contribution</b>This study makes three significant theoretical contributions:<b>The "Algorithm of Trust" Framework:</b> Proposes trust as a sequenced set of socio-technical prerequisites rather than a downstream variable in technology acceptance models<b>Contextual Intelligence Concept:</b> Identifies the critical clash between algorithmic systems and local contextual knowledge in resource-constrained settings<b>Professional Autonomy Preservation:</b> Documents nurses' insistence on an "override mandate" as essential for maintaining professional jurisdiction and accountability<b>Key Findings</b>Analysis revealed four interconnected themes that constitute the "Algorithm of Trust":<b>The Primacy of Explainable Trust:</b> Transparency, local validation, and professional override capability as non-negotiable requirements<b>AI as a Double-Edged Sword:</b> A nuanced risk-reward calculus balancing efficiency benefits against accountability paradoxes<b>Contextual Intelligence vs. Algorithmic Ignorance:</b> The necessity for AI systems to understand local infrastructure, cultural norms, and resource constraints<b>A Mandate for Co-Design:</b> Essential requirements for training, participatory development, and ethical governance<b>Significance and Implications</b>This research provides:<b>Practical Guidance:</b> Clear implementation pathways for AI integration in challenging environments<b>Policy Recommendations:</b> Framework for ethical governance and participatory design in global health technology<b>Theoretical Advancement:</b> Extends Science and Technology Studies (STS) and sociology of professions literature to AI contexts<b>Global Relevance:</b> Universal principles applicable to any setting characterized by high stakes, professional expertise, and systemic vulnerability<b>Materials Included</b><b>Interview Guide:</b> Comprehensive 32-question protocol exploring awareness, trust factors, benefits/risks, and contextual considerations<b>De-identified Dataset:</b> Complete demographic and thematic coding data for 25 participants<b>Data Dictionary:</b> Detailed documentation of variables, coding procedures, and quality assurance measures<b>Analysis Scripts:</b> Both R and Python scripts for statistical analysis, visualization, and reproducibility<b>Methodological Documentation:</b> Full transparency in analysis procedures and ethical considerations<b>Methodological Rigor</b><b>Credibility Strategies:</b> Prolonged engagement, peer debriefing, member checking (5 participants), triangulation, reflexivity, audit trail<b>Reporting Standards:</b> COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist adherence<b>Analytical Transparency:</b> Complete documentation of coding decisions and theme development processes<b>Contextual Importance</b>This study is situated within the Palestinian healthcare system—a setting characterized by profound resource constraints, political instability, and fragmented service delivery. This context serves as a "critical case" (Flyvbjerg, 2006) that illuminates fundamental trust prerequisites that might remain obscured in more resourced environments but are essential for equitable global technology integration.<b>Access and Usage</b><b>Public Materials:</b> Interview guide, data dictionary, analysis scripts, and de-identified dataset<b>Restricted Materials:</b> Interview transcripts available upon reasonable request with ethical approvals<b>Citation Requirement:</b> Appropriate attribution to original authors required<b>License:</b> CC-BY 4.0 for academic and research use<b>Research Team</b><b>Principal Investigator:</b> Ibrahim Aqtam (Palestinian nurse with clinical experience, providing insider perspective)<b>Co-Author:</b> Mustafa Shouli (Qualitative health researcher providing analytical validation)<b>Institutional Affiliation:</b> Ibn Sina College for Health Professions, Nablus University for Vocational and Technical Education<b>Citation</b>Aqtam, I., & Shouli, M. (2024). "Trust is the First Algorithm": A Socio-Technical Framework for Generative AI Integration in a High-Stakes, Resource-Constrained Context. <i>[Journal name pending publication]</i>. Supplementary materials available at: [Figshare DOI/LINK]<br><b>Keywords for Discovery</b>Artificial Intelligence Trust, Healthcare Technology Adoption, Resource-Constrained Settings, Palestinian Healthcare, Nursing Professional Autonomy, Qualitative Health Research, Socio-technical Systems, Generative AI Ethics, Implementation Science, Critical Case Study Methodology, Reflexive Thematic Analysis, Participatory Design, Algorithmic Accountability, Cultural Competence in AI, Global Health Technology Equity<br>