Expert Judgment Research Articles

Is there a maximum transfer time for safe planned home births? Building evidence to

Abstract Evidence-based assessment of suitability for planned home birth (PHB) is a critical component of safe, effective home birth services. This study aimed to build an evidence base to inform transfer time recommendations for the Health Service Executive (HSE) Home Birth Service in Ireland. Transfer rates, time and distance were described for women who began labour at home with the HSE Home Birth Service between 2012 and 2021. Binary logistic regression was used to identify factors associated with intrapartum transfer and to examine the relationship between transfer time and outcomes. A survey on home birth policy and practice was issued to informants in countries offering comparable services to the HSE. Of 2,000 women initiating labour at home, 376 (18.9%) required intrapartum transfer, with transfer rates of 48.2% and 10.1% for primiparous and multiparous women, respectively. Primiparity (aOR: 8.95, 95% CI 6.89-11.7, p < 0.001) and maternal body mass index ≥30 (aOR: 1.93, 95% CI 1.14-3.20, p = 0.013) significantly increased the odds of intrapartum transfer. Serious adverse outcomes were rare in those requiring intrapartum transfer. No statistically significant increase in poor outcomes was detected with increasing transfer time; however, statistical power to detect small but important effects was limited. In other home birth services, transfer time is explicitly considered when assessing suitability; information provided to women includes details on transfer rates and times. While this study did not find a threshold time associated with increased risk of adverse outcomes for PHB, it has highlighted the significance of transfer time in shared decision-making on birthplace. Women considering PHB should receive parity-specific information on outcomes including transfer rates. This work highlights the pivotal role of Public Health in ensuring that evidence is combined with expert judgement and womens’ preferences to provide high quality, accessible, person-centred services. Key messages • Transfer time plays a key role in supporting shared decision-making for women considering a home birth. • This study identified opportunities to extend the comprehensiveness of assessment of suitability for planned home births using local and international evidence.

EvalDepApps: How to evaluate depression management based on evidence and end users

Abstract The use of apps is increasing in the field of mental health due to their ease of use and accessibility, although there is not enough evidence on their effectiveness and safety. EvalDepApps aims to develop an evaluation tool for depression management apps. A systematic review with meta-analysis (SRMA) was performed to evaluate the efficacy and safety of apps for depression, and to identify the evaluation criteria used. PRISMA methodology was followed. The MEDLINE, PsycINFO, and Embase databases were consulted. The risk of bias was assessed with the RoB2 scale. An online 2 rounds Delphi was carried out to prioritize the most relevant criteria identified. 44 people (26 professionals, 18 patients) were invited. They were asked to rate the importance of each criterion on a Likert scale (1 - 6). Those that obtained a high consensus were selected; those with a medium were submitted to the 2nd round. Empathization (6) and co-design (6) sessions were held with patients (23) and professionals (33) in Catalonia, the Canary Islands and Andalusia to identify what relevant aspects the tool should have. Twenty-nine studies were included in the SRMA (67% unclear bias), finding a significant effect of mHealth interventions in reducing depressive symptoms compared to non-active control (Hedges g = −0.62, 95% CI: −0.87 to − 0.37, I2 = 87%). In Round 1 of the Delphi (59% participation) 24 criteria obtained a high consensus, 20 a medium and 7 a low. In Round 2 (52% participation), 4 criteria reached high consensus. The empathy sessions showed that the actions most requested by patients were reduce anxiety, and information about their condition; for professionals, suicide prevention. Regarding co-design, it was proposed that the tool provide ranking of the apps, recommendation systems and a very visual format. The RSMA and Delphi guarantee that the tool will be based on scientific evidence and expert judgment, while empathization and co-design that it will fit with the needs of end users. Key messages • It is relevant to evaluate the quality of health apps, especially those addressed to vulnerable populations such as people with depression. • It is crucial that the development of evaluation tools for digital health interventions will be developed based on evidence-based but also includes end users.

Informal digital learning of English in teachers: development and validation of a scale

Abstract Informal digital learning of English (IDLE) is a promising way of learning English that has received growing attention in recent years. It has positive effects on English as a foreign language (EFL) learners and also creates valuable opportunities for EFL teachers to improve their teaching skills. However, there has been a lack of a valid and reliable scale to measure IDLE among teachers in EFL contexts. To address this lacuna, this study aims to develop and validate a scale to measure IDLE for EFL teachers in Iran. For this purpose, a nine-step rigorous validation procedure was undertaken: administering pilot interviews; creating the first item pool; running expert judgment; running interviews and think-aloud protocol; running the pilot study; performing exploratory factor analysis, Cronbach’s alpha, and confirmatory factor analysis; creating the second item pool; conducting expert reviews; and performing translation and translation quality check. Findings yielded a 41-item scale with six subscales: IDLE-enhanced benefits (12 items), IDLE practice (five items), support from others (nine items), authentic L2 experience (three items), resources and cognition (four items), and frequency and device (eight items). The scale demonstrated satisfactory psychometric properties such that it can be used for research and educational purposes in future.

Effects of Task Complexity and L2 Proficiency on Functional Adequacy in L2 Writing

ABSTRACTThis study examined how task complexity and second language (L2) proficiency impact functional adequacy (FA) among Chinese university English learners. Eighty‐nine Chinese undergraduate students from two first‐year and two third‐year classes participated in the study. Each participant completed two timed argumentative writing tasks with varying degrees of task complexity manipulated by altering the number of argument elements and reasoning demands, drawing upon the cognition hypothesis. The task‐complexity manipulations were validated by post‐task questionnaires and expert judgments. Learners’ productions were assessed by two experts using the FA rating scale. Results showed as task complexity increased, there was a significant decrease in scores across all four dimensions of FA. Additionally, a positive effect of L2 proficiency was observed on scores in two dimensions of FA, that is, comprehensibility and coherence and cohesion. However, this was not the case for content and task requirements. Importantly, the analysis revealed a significant interaction effect between task complexity and L2 proficiency, indicating that the impact of task complexity on FA varied depending on the learners’ proficiency levels. Specifically, higher proficiency learners demonstrated greater adaptability to complex tasks compared to their lower proficiency counterparts. These findings highlight the significance of manipulating task complexity and considering learners’ L2 proficiency in teaching L2 writing to enhance learners’ FA performance.

Biological literacy skills of senior high school student using local potential-based biodiversity question sets

One of the efforts to improve the quality of education in Indonesia is through learning by integrating environmental culture. Environmental culture can be packaged in more meaningful learning through biological literacy. This study aims to assess the biological literacy skills of high school students using local potential-based biodiversity question sets. This study includes research with quantitative descriptive methods that aim to describe the biological literacy skills of high school tenth grade students. Biological literacy research data consists of 4 indicators, namely nominal, conceptual, multidimensional and functional. The number of participants was 60 high school students. The initial stage used expert judgment for content validation which was tested by 3 expert validators. Construct validation was carried out based on data collection with the results of 12 question sets that were declared valid. Data analysis of the percentage of students who answered correctly on biological literacy indicators were all at a high level, namely on nominal indicators of 69.37%, conceptual indicators of 70%, functional indicators of 66.87%, and multidimensional indicators of 70%. It is concluded that the local wisdom-based biodiversity question set can be used to measure students' biological literacy skills in schools.

Expert elicitation of state shifts and divergent sensitivities to climate warming across northern ecosystems

Northern regions are warming faster than the rest of the globe. It is difficult to predict ecosystem responses to warming because the thermal sensitivity of their biophysical components varies. Here, we present an analysis of the authors’ expert judgment regarding the sensitivity of six ecosystem components – permafrost, peatlands, lakes, snowpack, vegetation, and endothermic vertebrates – across northern landscapes ranging from boreal to polar biomes. We identified 28 discontinuous component states across a 3700 km latitudinal gradient in northeastern North America and quantified sensitivity as the transition time from an initial to a contrasting state following a theoretical step change increase in mean annual air temperature of 5 °C. We infer that multiple interconnected state shifts are likely to occur within a narrow subarctic latitudinal band at timescales of 10 to more than 100 years, and response times decrease with latitude. Response times differ between components and across latitudes, which is likely to impair the integrity of ecosystems.

Transformation of Traditional Corporate Tax Planning into AI-Driven Corporate Tax Planning

Corporate tax planning has traditionally relied on manual processes and expert judgment to minimize tax liabilities. However, the rise of artificial intelligence (AI) presents new opportunities to transform these practices through automation, real-time analysis, and predictive capabilities. To conduct this research, a comprehensive review of academic literature, case studies, and existing AI tools in tax planning was carried out. The proposed AI-based tax planning model was developed based on these findings and validated through simulations and expert feedback. The study also examined the process of integrating AI systems with existing corporate tax structures to assess the feasibility and effectiveness of implementation. Results show that AI-driven corporate tax planning significantly enhances accuracy and efficiency compared to traditional methods. The AI model provides real-time analysis and predictive insights, enabling businesses to optimize tax strategies while ensuring compliance with evolving regulations. Tax authorities also benefit from AI implementation, gaining more transparent and equitable tax structures, along with stronger supervision capabilities. The proposed model can be developed into customized software tailored to individual companies, seamlessly integrating with existing corporate systems. These findings indicate that AI has the potential to revolutionize corporate tax planning by streamlining tax management and optimizing outcomes. For businesses, AI enables personalized tax strategies that adapt to specific data and regulations, improving both compliance and long-term tax optimization.

Backtesting the iconic Polyfytos balanced-cantilever bridge to enhance condition assessment

This paper introduces a comprehensive framework for reliable damage assessment and decision-making for bridge intervention planning. By utilizing historical data and 3D-UAV photogrammetry, a detailed 3D numerical model is created, incorporating advanced modeling and scenario-based approaches. The methodology is applied to the Polyfytos bridge in Greece, which faces significant vertical deck deflections that disrupt traffic and impact local communities. This approach offers a reliable, cost-effective, and efficient process, allowing stakeholders to make informed decisions with minimal physical interaction with the bridge. The integration of advanced modeling with in-situ measurements and expert judgment is emphasized. For the Polyfytos bridge, the framework helps prioritize retrofits and assess the deck’s remaining strength under various loads, providing estimates of its overstrength. This aids in evaluating the bridge’s ability to handle additional vertical loads through pushdown analysis. Overall, the framework is valuable for rapid infrastructure assessment, identifying damage patterns, severity, and the urgency of interventions related to structural strength.

Model-Centric Integration of Uncertain Expert Knowledge into Importance Sampling-Based Parameter Estimation

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated.

Combined exercise Intervention protocol validation in sedentary workers health risk with Nonspecific Chronic low back pain People

Background: Most chronic low back pain is nonspecific as the cause of the pain is unknown. Hence exercise protocol focussing on one area of fitness is not appropriate. A general exercise programme that combines muscular strength, flexibility and aerobic fitness would benefit the rehabilitation of Nonspecific Chronic Low Back Pain and its health risk. The present study aimed to validate the content of the combined exercise intervention protocol. Methods: The development and expert judgement stages are part of the content validation process. The development of the exercise protocol was based on a comprehensive literature review. The protocol was created in a step-by-step progressive manner. Exercises for flexibility (20 minutes), stabilisation and strengthening (20 minutes), and aerobic (20 minutes) make up the three main parts of the exercise protocol. 12 independent professionals with a minimum of 10 years of experience were invited. Ten of the twelve specialists consented to take part in the study. In India, all of the professionals were engaged in orthopaedic and musculoskeletal physical therapy. There are two professors, three associate professors, and five senior physiotherapists among the experts. Responses were gathered by mail. During the validation phase, The Exercise protocol was graded by the experts on relevance and ease of performance using a 5-point scale. After collecting the responses content validity index and kappa values were calculated. Results: 37 out of 39 items in the combined exercise intervention protocol showed good agreement. Treadmill walking with 60-75% and 80% and above target heart rate showed a kappa value of 0.66 for relevance and 0.50 for ease of performance. Conclusions: The designed exercise protocol exhibited good content validity.

Subjectivity of visual assessments in FOCUS kinetics and acceptability of first-order fits for regulatory modelling

The degradation half-life (DegT50) of a substance in soil plays an important role in the approval process of a plant protection product and is a sensitive input parameter for regulatory models. It is usually derived through least squares optimizations of mathematical models to measured degradation data according to EU FOCUS degradation kinetics guidance. A strong consensus on degradation parameters provides a solid foundation for parts of the environmental risk assessment. The DegT50 of a substance for regulatory modeling is preferably derived from a single first-order (SFO) model as this is currently the only kinetic model implemented in EU regulatory models of the environmental fate of pesticides. However, kinetic optimisation tools do not always provide a regulatory acceptable SFO fit even though a visual inspection of the data suggests it may be possible. It was therefore hypothesized that more acceptable SFO fits might be achieved by adapting the objective function that is minimized during the optimization.Eight objective functions with varying weightings were tested on 29 laboratory soil degradation datasets. A web-based app was developed to allow experts in environmental safety of plant protection products to visually assess the goodness of fits resulting from different objective functions. The visual assessments and a quantitative metric, newly introduced in the proposed update of the FOCUS guidance, show that the acceptability of SFO fits can be increased, but no single objective function exclusively improves all fits. The assessment reveals that expert judgment is very subjective. Participants tended to change their mind when judging the acceptance of a fit, assumingly caused by a learning curve or a period of calibration.It is concluded that different objective functions could be considered in the kinetic assessment as it can improve the acceptability of SFO fits and hence endpoints for regulatory modeling. This study reveals that various qualitative factors influence the visual judgment of experts when performing a kinetic modeling assessment. The proposed quantitative metric seems to be in alignment with the visual assessment of fits to derive modeling endpoints and a promising step toward less subjective kinetic modeling assessments.

Intelligent prediction of rail corrugation evolution trend based on self-attention bidirectional TCN and GRU

Analyzing the evolution trend of rail corrugation using signal processing and deep learning is critical for railway safety, as current traditional methods struggle to capture the complex evolution of corrugation. This present study addresses the challenge of accurately capturing this trend, which relies significantly on expert judgment, by proposing an intelligent prediction method based on self-attention (SA), a bidirectional temporal convolutional network (TCN), and a bidirectional gated recurrent unit (GRU). First, multidomain feature extraction and adaptive feature screening were used to obtain the optimal feature set. These features were then combined with principal component analysis (PCA) and the Mahalanobis distance (MD) method to construct a comprehensive health indicator (CHI) that reflects the evolution of rail corrugation. A bidirectional fusion model architecture was employed to capture the temporal correlations between forward and backward information during corrugation evolution, with SA embedded in the model to enhance the focus on key information. The outcome was a rail corrugation trend prediction network that combined a bidirectional TCN, bidirectional GRU, and SA. Subsequently, a multi-strategy improved crested porcupine optimizer (CPO) algorithm was constructed to automatically obtain the optimal network hyperparameters. The proposed method was validated with on-site rail corrugation data, demonstrating superior predictive performance compared to other advanced methods. In summary, the proposed method can accurately predict the evolution trend of rail corrugation, offering a valuable tool for on-site railway maintenance.

The TOPSIS method: Figuring the landslide susceptibility using Excel and GIS

The current study introduced Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enhance landslide susceptibility. It determines the relative distance of each alternative from the ideal best and ideal worst value. The ArcGIS environment was used to prepare eleven landslide conditioning factors, while raster values were extracted for the decision matrix preparation. We utilized subjective expert judgment to create a weighted matrix that considers the roles of each conditioning component. In addition, a Euclidean distance was measured from each alternative to the ideal best and worst values. The relative closeness value (Ri) has been used to prepare the landslide susceptibility index by the inverse distance weighting (IDW) interpolation. Furthermore, the precision of the landslide susceptibility was justified by area under curve-receiver operating characteristic (AUC-ROC) which was 0.987. Hence, multi-criteria decision-making (MCDM) techniques like the TOPSIS method are very useful for natural hazard mapping.•The simplified TOPSIS approach described by Hwang and Yoon (1981) is applied in this study. The criteria have been categorized and assigned weights based on expert judgment and previously published material.•The TOPSIS approach and GIS integration has significantly enhanced the creation of a landslide susceptibility map for a sensitive area.•The method is easiest and suitable for short term operation research.

Softskill Base Arabic Language Teaching Model in Improving Talaqul Kalam for College Student in Universitas Sains Al-Quran Jawa Tengah Wonosobo

Arabic language learning has a major problem related to its renewal. Arabic language learning is always identical with grammatical (Qawa’id) aspects which seem to be rigid, complicated, and confusing. There are two major Arabic language learning orientations: as body of knowledge and mastery. The purpose of this research is to produce an Arabic language learning model based on soft skills to effectively improve the students’ Talaqah al kalaam at Universitas Sains Al-Qur’an (UNSIQ), Wonosobo, Central Java, Indonesia. This research was considered as a field study in the form of Research and Development (R&D) involving students from each faculty of Universitas Sains Al-Qur’an (UNSIQ), Wonosobo, Central Java, Indonesia. The research data were collected through observation, in-depth interview, questionnaire, test, and documentation. The collected data were then analyzed through several stages: (a) initial-product test analysis, (b) limited data trial result analysis, (c) extensive data trial result analysis, and (d) student learning response analysis. The research results showed that the Arabic language learning model based on soft skills had fulfilled the validity elements through Expert Judgment method and been considered very effective proven by the significant improvement in post-test results. This model was also proven practical proven by the test results showing that the supporting instruments, such as Course Learning Unit, Learning Module, Semester Learning Plan, Test Instrument, and Learning Implementation Sheet, were very good.

Are customers naturally attracted to brands? A core social motives scale for measuring brand engagement

PurposeTo extend consumer–brand relationship research to the satisfaction of social needs, we introduce a new construct to assess consumer propensity to develop brand relationships based on the satisfaction of social needs through brands and provide a scale to measure this construct.Design/methodology/approachWe implement a two-stage procedure based on the literature and expert judgments to formulate questions based on questionnaire data, and refinement processes and validation analyses are applied to measure the validity and reliability of the scale.FindingsWe developed a scale for measuring consumer propensity for establishing brand relationships that covers five dimensions: belonging, control, understanding, self-enhancement and trust. The scale was validated and found to be reliable.Research limitations/implicationsThis research could benefit from model testing using samples from outside the USA, thus incorporating the role of culture and considering behavioral outputs.Practical implicationsThe identification and operationalization of the propensity to relate to brands are important in market segmentation for more efficiently deepening brand relationships. Additionally, this conceptualization improves our understanding of the elements that contribute to the relationship between brands and consumers: belonging, control, understanding, self-enhancement and trust.Originality/valueAlthough the consumer–brand relationship is recognized in the literature, the satisfaction of social needs via this relationship has been ignored. This paper aims to enhance our understanding of person–brand relationships by proposing a scheme for analyzing the degree of need satisfaction facilitated by this relationship.

Enhancing the accreditation of Indonesian private universities through the integration of EduQual and accreditation standards of the BAN-PT

Higher education institutions in Indonesia are currently facing significant challenges in maintaining public trust, requiring them to uphold autonomy, transparency, accountability, and continuously meet standards for quality assurance and improvement. This research aims to identify and address the unmet quality aspects in education to elevate the accreditation of private universities from C/Good to B/Very Good or even A/Excellent. By integrating the EduQual model with certification requirements from the Board of National Accreditation for Higher Education (Badan Akreditasi Nasional Perguruan Tinggi, BAN-PT), the research is supported by comprehensive studies of university accreditation reports, questionnaires, focus group discussions (FGD), and expert judgment through interviews and consultations. The data analysis employs gap analysis, importance-performance analysis, and quality function deployment. The study identified twenty-one priority improvement points to enhance accreditation, with six key improvements prioritized from the ninety indicators examined based on customer needs analysis. In the accreditation of private universities, the unmet aspects of education quality lie in the dimension of physical facilities, particularly human resources, as well as in the dimension of personal development, especially in the criteria for research and community service. The study recommends prioritizing investments in human resource development and strengthening research and community service initiatives, as these are critical areas where private universities fall short in meeting accreditation standards and fulfilling educational quality expectations.

Priority Variables for Handling Slum Settlements Based on the Tourism Village Concept in Sedau, Singkawang City

The issue of slum settlements in Singkawang City has garnered government attention, with Sedau identified as the third priority area for intervention. However, many revitalization efforts fail due to mismatched methods that do not consider environmental, social, and economic characteristics. This study aims to identify priority variables for addressing slum settlements based on the concept of developing a tourism village to improve residents' quality of life and support local economic development. The research method employed is expert judgment through the Analytical Hierarchy Process (AHP), which helps evaluate relevant variables. The findings reveal four main priority variables: good accessibility, a unique tourism village concept, provision of clean water, and waste management. These findings provide important insights for decision-making in planning interventions. Recommendations are directed at the government, development sectors, and the Sedau community, emphasizing the need for better planning, particularly regarding accessibility and infrastructure such as roads, water supply, and waste management. The study also notes its limitations to provide context for the generalization of results and to guide future research. Keywords: planning, slum settlement, tourism village, tourism

DEVELOPMENT OF A COMPETENCY MATRIX IN A FRAMEWORK FOR THE PRACTICE OF RATIONAL MEDICINE USE IN THE TREATMENT OF SYSTEMIC ARTERIAL HYPERTENSION

Background: The use of medicines plays an important therapeutic role in the care of hypertension. Nonetheless, the inadequacies related to pharmacotherapy are still a major challenge to health, and the lack of systematisation of the competencies to be acquired during the training of health professionals undermines practices involving rational medicine use in the treatment of hypertension. This study was intended to develop and validate a competency matrix that can be used in the education and training of health professionals, contributing to the practice of rational medicine use (RMU) in the treatment of systemic arterial hypertension (SAH). Methods: This is applied research containing an integrative review and exploratory research with the presence of a systematic search, following guidelines proposed by the Cochrane Database of Systematic Reviews and the PRISMA group, and the building of the competency matrix was carried out through a focus group with researchers who work in the area registered in two research groups certified by an agency linked to the Brazilian Ministry of Education. The matrix was validated using the Delphi method by expert judges selected using Fehring’s technique and scoring criteria. Results: The panel of experts reached a consensus on the relevance of four intrinsic dimensions containing seven competencies and 31 knowledge, skills and attitudes of the RMU in SAH and three extrinsic dimensions that encompassed nine competencies and 22 knowledge, skills and attitudes of the RMU in SAH, with a frequency of agreement greater than 80%.

Risk Assessment of Employing Digital Robots in Process Automation

Using digital technologies is essential to gain a competitive advantage in the global market by adapting to new business models. While digital technologies make business processes efficient, they enable companies to make faster and more accurate decisions by automating daily and routine process tasks. Robotic process automation (RPA) automates routine and repetitive business processes, allowing many jobs performed by humans to be performed faster. This way, advantages such as reduced error rates, reduced costs, increased production speed, and labor productivity are provided. For the successful implementation of RPA, potential risks need to be considered. In this study, failure mode and effect analysis (FMEA) based on decomposed fuzzy sets (DFSs), a new extension of intuitionistic fuzzy sets, has been used to evaluate subjectiveness in expert judgments. Differing from the other extensions of fuzzy set theory, the advantage of DFSs is to simultaneously consider decision-makers’ optimistic and pessimistic answers. Thus, the answer given by the decision-maker to the positive and negative questions on the same subject defines the indeterminacy of the decision-maker, and the method takes this indeterminacy into account in the evaluation. This study assesses and evaluates the potential risks of six digital robots in process automation. Thirteen risks were individually assessed for each automated process. This study found “Sustainability challenge” critical in three processes, “Absence of governance management” in two, and “Security“ in one. Variability in risk importance arose from process vulnerabilities.

Toward holistic neighbourhood sustainability assessment: integrating fuzzy Delphi method for sustainable indicator selection for Aizawl city

ABSTRACT Neighbourhood Sustainability Assessment (NSA) is vital in navigating the complex dynamics of social, environmental, and economic factors in localised settings, especially amidst the challenges of urbanisation, resource scarcity, and climate change. NSA tools have emerged as pivotal instruments in integrating sustainability into urban development, recognising neighbourhood as fundamental units for systemic sustainability approaches. However, existing NSA methodologies tend to favour environmental aspects, prompting a call for more balanced approaches encompassing social and economic dimensions. The study underscores the need for NSA tools to evolve towards context-sensitive methodologies for broader applicability across diverse urban landscapes. Through a literature review, the study highlights the importance of integrating community perspectives, sustainability indicators, and context-specific approaches in NSA, advocating for holistic strategies to drive sustainable urban development agendas. This study presents a methodological framework for selecting neighbourhood sustainability indicators in Aizawl, the capital city of Mizoram which is located in N-E India, by incorporating community perspectives and expert judgments. The methodology involves several steps: review of existing NSA frameworks to select comprehensive indicators, perception and public participation surveys to identify local indicators, engagement of experts through the Fuzzy-Delphi Method, and application of the Analytic Hierarchy Process (AHP) for indicator prioritisation. Through these steps, 47 essential indicators were selected, covering social, economic, environmental and governance aspects. The study emphasises the importance of combining bottom-up community input with top-down-expert knowledge for comprehensive and reliable sustainability assessments. Challenges such as energy and water resource deficiencies are highlighted, underscoring the need for balanced approaches to neighbourhood sustainability planning.