Western University

Surgeons pursuing improvement in the aesthetic outcomes of their cleft lip repairs may benefit from a granular scale evaluating individual objectives of the repair. A working group of 9 surgeons convened to develop an assessment scale for nasolabial aesthetics after unilateral cleft lip repair. The group identified objectives of the repair that could be evaluated using two-dimensional facial photographs. Scale items were developed to appraise success or failure in achieving each objective. Scale items were iteratively tested and refined. The scale was subsequently implemented as part of a Continuing Medical Education course that included self-evaluation and peer-to-peer education, culminating in the formation of individual plans for improvement. Twelve distinct objectives of unilateral cleft lip repair were identified, of which 10 could be evaluated using photographs routinely obtained in clinical practice. A comprehensive scale was developed, incorporating these 10 objectives. Each scale item takes the form of a binary (yes/no) question evaluating a specific aesthetic concept, with accompanying reference images. Intrarater reliability for each item ranged from moderate to substantial (kappa value, 0.57 to 0.81). Interrater reliability ranged from fair to substantial (kappa value, 0.27 to 0.81). When implemented in a Continuing Medical Education course, the scale enabled surgeons to identify specific opportunities for improvement in their repair and specific surgical maneuvers to adopt in pursuit of these improvements. A new scale for evaluating outcomes of unilateral cleft lip repair is presented. The scale provides specific, actionable evaluations for individual objectives of the repair.

Artificial intelligence (AI) is increasingly recognized as a transformative force in public administration, yet its adoption in local governments, particularly in developing countries, remains fragmented and uneven. This study examines the administrative readiness of Thai local governments to integrate AI into public management systems, addressing gaps in existing research that often emphasize technical dimensions while neglecting organizational, cultural, and behavioral factors. Employing a qualitative case study approach, data were collected through semi-structured interviews with 25 key informants and analyzed through thematic analysis supported by triangulation and member checking. Findings revealed that AI readiness extends beyond technological capacity to encompass eight interrelated dimensions organized according to the Technology-Organization-Environment (TOE) framework: one technological dimension (technology and infrastructure), five organizational dimensions (human resources, change management readiness, financial resources, leadership and vision, organizational culture), and two environmental dimensions (environmental context, and policy and governance). Of these eight dimensions, two are AI-specific (technology and infrastructure, policy and governance), three are partially AI-specific (human resources, organizational culture, environmental context), and three represent general digital transformation requirements adapted for AI contexts (financial resources, leadership and vision, change management). Analysis through the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) demonstrated that hedonic motivation, price value, and habit formation serve as critical behavioral drivers translating structural readiness into sustained use. Strategic directions proposed through the McKinsey 7S Framework emphasize coherent alignment across organizational elements. This study contributes an integrated framework capturing contextual realities of local governments in developing settings, advancing theoretical discourse while offering practical guidance for fostering sustainable, citizen-centered AI transformation.