Evaluation Schema Research Articles

A tale of two prisons: Investigating the social climate of small prisons using a mixed methods design

Prison climate can have considerable impact on the behaviours of incarcerated individuals both in custody and on release. This study is the first study to use a mixed methods approach to explore the perceptions of prison climate among prisoners and staff at two small Australian prisons. The quantitative research component measured prisoner ( n = 117) and staff ( n = 117) ratings of prison climate using the Essen Climate Evaluation Schema (EssenCES) questionnaire. Results highlighted that, overall, prison staff rated prison climate as more supportive than prisoners did and that prisoners and staff in the lower security prison rated the prison climate as safer and more cohesive than prisoners and staff in the higher security prison. The qualitative research component consisted of semi-structured interviews with prisoners ( n = 16) and a range of prison staff ( n = 17). The qualitative analysis revealed a more detailed understanding of the perceived differences between the two prisons with most participants articulating agreed values and behaviours that are important to a more positive and supportive prison climate. Barriers to a positive prison climate were also identified. This research contributes a unique perspective on prison climate by using both qualitative and quantitative measure to examine the perceptions of those living and working in small prisons.

A Multidimensional Approach for Evaluating Reality in Social Media: Mixed Methods Study.

Misinformation is a threat to public health. The effective countering of misinformation may require moving beyond the binary classification of fake versus fact to capture the range of schemas that users employ to evaluate social media content. A more comprehensive understanding of user evaluation schemas is necessary. The goal of this research was to advance the current understanding of user evaluations of social media information and to develop and validate a measurement instrument for assessing social media realism. This research involved a sequence of 2 studies. First, we used qualitative focus groups (n=48). Second, building on the first study, we surveyed a national sample of social media users (n=442). The focus group data were analyzed using the constant comparison approach. The survey data were analyzed using confirmatory factor analyses and ordinary least squares regression. The findings showed that social media reality evaluation involves 5 dimensions: falsity, naturality, authenticity, resonance, and social assurance. These dimensions were differentially mapped onto patterns of social media use. Authenticity was strongly associated with the existing global measure of social media realism (P<.001). Naturality, or the willingness to accept artificiality and engineered aspects of social media representations, was linked to hedonic enjoyment (P<.001). Resonance predicted reflective thinking (P<.001), while social assurance was strongly related to addictive use (P<.001). Falsity, the general belief that much of what is on social media is not real, showed a positive association with both frequency (P<.001) and engagement with (P=.003) social media. These results provide preliminary validity data for a social media reality measure that encompasses multiple evaluation schemas for social media content. The identification of divergent schemas expands the current focus beyond fake versus fact, while the goals, contexts, and outcomes of social media use associated with these schemas can guide future digital media literacy efforts. Specifically, the social media reality measure can be used to develop tailored digital media literacy interventions for addressing diverse public health issues.

CellMarkerPipe: cell marker identification and evaluation pipeline in single cell transcriptomes

Assessing marker genes from all cell clusters can be time-consuming and lack systematic strategy. Streamlining this process through a unified computational platform that automates identification and benchmarking will greatly enhance efficiency and ensure a fair evaluation. We therefore developed a novel computational platform, cellMarkerPipe (https://github.com/yao-laboratory/cellMarkerPipe), for automated cell-type specific marker gene identification from scRNA-seq data, coupled with comprehensive evaluation schema. CellMarkerPipe adaptively wraps around a collection of commonly used and state-of-the-art tools, including Seurat, COSG, SC3, SCMarker, COMET, and scGeneFit. From rigorously testing across diverse samples, we ascertain SCMarker’s overall reliable performance in single marker gene selection, with COSG showing commendable speed and comparable efficacy. Furthermore, we demonstrate the pivotal role of our approach in real-world medical datasets. This general and opensource pipeline stands as a significant advancement in streamlining cell marker gene identification and evaluation, fitting broad applications in the field of cellular biology and medical research.

The evaluation and optimization of the agricultural sustainable development based on a data-driven approach: A case from Northern Anhui

The sustainable advancement of agriculture stands as the fundamental cornerstone of sustainable human progress. This study introduces a data-centric methodological framework founded upon the holistic delineation of measurement, feature assessment, and pathway enhancement for agricultural sustainability. Initially, the research articulates a comprehensive evaluative schema incorporating sub-dimensions encompassing agricultural production, agricultural economics, the agricultural resource environment, and rural society, grounded in sustainable development theory. Subsequently, it devises a methodological apparatus for assessing and enhancing sustainable development capabilities, employing entropy evaluation methods and exploratory spatial data analysis techniques. Employing North Anhui as a case study, the viability of this approach is substantiated. The empirical inquiry conducted within this article operationalizes comprehensive evaluation and explores pathways for optimizing agricultural sustainability, focusing on the period spanning 2011 to 2020 in Northern Anhui. The findings affirm the feasibility and efficacy of the data-driven approach. Recommendations derived from the empirical exploration of agricultural sustainability pathways at the local level offer valuable insights for governmental authorities and policymakers. This research endeavor could be extrapolated to other geographical locales worldwide, fostering innovative strides in the sustainable development of regional agriculture.

Social Distance Behind Bars: Vergleich der Wahrnehmung des sozialen Klimas während der COVID-19-Pandemie zwischen Insassen und Vollzugsbediensteten in deutschen Gefängnissen

AbstractThe COVID-19 pandemic presented challenges to prison officers and inmates alike. Since it impacted all areas of the working and living environment, it was assumed that the social climate in prison changed as a result. As part of the Corona Behind Bars project, this study had two objectives: first, to compare how the social climate (Essen Climate Evaluation Schema) is perceived by inmates (N = 956) and prison officers (N = 502) as a result of the pandemic. Second, to analyze the impact of the burden of contact restrictions between inmates and prison officers on their respective perceptions of the social climate. In line with the differences between inmates and prison officers reported in the literature, inmates reported significantly higher values on the dimension experienced safety than did prison officers, while prison officers reported significantly higher values on the dimension therapeutic hold. No significant difference was found for the dimension inmate cohesion. Compared to norm values both the prison officers and the inmates rated the social climate as clearly below average. The moderation analysis revealed an interaction of status group × burden of contact restriction: The impact of the burden of contact restriction on social climate perception was more pronounced for inmates than for prison officers. Overall, the results suggest that the perception of the social climate was adversely affected by the pandemic for the prison officers and the inmates. However, the specific factors contributing to this deterioration appear to be group-specific.

Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

Improving large language models for clinical named entity recognition via prompt engineering.

The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets. This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance. We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, and tests from clinical notes in the MTSamples corpus, following the 2010 i2b2 concept extraction shared task, and (2) to identify nervous system disorder-related adverse events from safety reports in the vaccine adverse event reporting system (VAERS). To improve the GPT models' performance, we developed a clinical task-specific prompt framework that includes (1) baseline prompts with task description and format specification, (2) annotation guideline-based prompts, (3) error analysis-based instructions, and (4) annotated samples for few-shot learning. We assessed each prompt's effectiveness and compared the models to BioClinicalBERT. Using baseline prompts, GPT-3.5 and GPT-4 achieved relaxed F1 scores of 0.634, 0.804 for MTSamples and 0.301, 0.593 for VAERS. Additional prompt components consistently improved model performance. When all 4 components were used, GPT-3.5 and GPT-4 achieved relaxed F1 socres of 0.794, 0.861 for MTSamples and 0.676, 0.736 for VAERS, demonstrating the effectiveness of our prompt framework. Although these results trail BioClinicalBERT (F1 of 0.901 for the MTSamples dataset and 0.802 for the VAERS), it is very promising considering few training samples are needed. The study's findings suggest a promising direction in leveraging LLMs for clinical NER tasks. However, while the performance of GPT models improved with task-specific prompts, there's a need for further development and refinement. LLMs like GPT-4 show potential in achieving close performance to state-of-the-art models like BioClinicalBERT, but they still require careful prompt engineering and understanding of task-specific knowledge. The study also underscores the importance of evaluation schemas that accurately reflect the capabilities and performance of LLMs in clinical settings. While direct application of GPT models to clinical NER tasks falls short of optimal performance, our task-specific prompt framework, incorporating medical knowledge and training samples, significantly enhances GPT models' feasibility for potential clinical applications.

Within-Modality Synthesis and Novel Radiomic Evaluation of Brain MRI Scans.

One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI) volume from T2-weight-Fluid-attenuated-Inversion-Recovery (FLAIR) and vice versa using Generative Adversarial Networks (GAN). To evaluate the proposed method, we propose a novel evaluation schema for generative and synthetic approaches based on radiomic features. For the evaluation purpose, we consider 510 pair-slices from 102 patients to train two different GAN-based architectures Cycle GAN and Dual Cycle-Consistent Adversarial network (DC2Anet). The results indicate that generative methods can produce similar results to the original sequence without significant change in the radiometric feature. Therefore, such a method can assist clinics to make decisions based on the generated image when different sequences are not available or there is not enough time to re-perform the MRI scans.

Quality assessment of VHH models

Heavy Chain Only Antibodies are specific to Camelid species. Despite the lack of the light chain variable domain, their heavy chain variable domain (VH) domain, named VHH or nanobody, has promising potential applications in research and therapeutic fields. The structural study of VHH is therefore of great interest. Unfortunately, considering the huge amount of sequences that might be produced, only about one thousand of VHH experimental structures are publicly available in the Protein Data Bank, implying that structural model prediction of VHH is a necessary alternative to obtaining 3D information besides its sequence. The present study aims to assess and compare the quality of predictions from different modelling methodologies. Established comparative & homology modelling approaches to recent Deep Learning-based modelling strategies were applied, i.e. Modeller using single or multiple structural templates, ModWeb, SwissModel (with two evaluation schema), RoseTTAfold, AlphaFold 2 and NanoNet. The prediction accuracy was evaluated using RMSD, TM-score, GDT-TS, GDT-HA and Protein Blocks distance metrics. Besides the global structure assessment, we performed specific analyses of Frameworks and CDRs structures. We observed that AlphaFold 2 and especially NanoNet performed better than the other evaluated softwares. Importantly, we performed molecular dynamics simulations of an experimental structure and a NanoNet predicted model of a VHH in order to compare the global structural flexibility and local conformations using Protein Blocks. Despite rather similar structures, substantial differences in dynamical properties were observed, which underlies the complexity of the task of model evaluation. Communicated by Ramaswamy H. Sarma

Developing ward social climate and sense of community within a high security forensic psychiatric service: Evaluating a sense of community and social climate intervention

ABSTRACT Inpatient ward social climate and sense of community can influence the recovery and care experiences of forensic mental health patients. This research evaluated an intervention designed to improve social climate and sense of community within a high-secure psychiatric hospital in the UK. The intervention was offered across six wards: two wards received the full intervention, two received the partial intervention, and two received no intervention (control). Social climate and sense of community were measured prior to the start of the intervention 6 and 18 months into the intervention, using the Essen Climate Evaluation Schema and the Sense of Community Index-2. Changes in frequency of disruptive behaviour were also monitored across time points. Results revealed that the full and the partial intervention groups reported greater feelings of social climate than the control group. This effect was also observed across time points, with higher social climate scores in the full and partial intervention groups compared to control group at 6 and 18 months. Furthermore, social climate scorespredicted a significant reduction in frequency of incidents across time in the full intervention. These findings provide longitudinal evidence of the importance of developing ward social climate and sense of community within forensic inpatient settings.

From where do legislators draw scientific knowledge? Organizations as scientific authorities in four countries' parliamentary debates.

Organizations far beyond traditional academic institutions have become prolific science producers, with many now providing evidence-based advice for national governments and policy-makers. Neo-institutional sociology explains organizations' growing investment in research activities and research-based policy advice by the all-embracing scientization and the expansion of the educated population, phenomena observable throughout the world. There is, however, considerably less knowledge about how the organizations' increased knowledge production and the supply of science-based policy advice are reflected in national policy-making, including the legislative work of parliaments, and to what extent distinct organizations are deemed authoritative in different countries. In this paper, we examine how different organizations are used as scientific authorities in parliamentary debates over new legislation. Drawing on analyses of 576 parliamentary debates from Australia, Finland, Kenya, and the United Kingdom, we study what organizations are acknowledged as scientific authorities and the relative weight of different organization types in the context of political debates over new legislation. The results reveal that while organizations in general are frequently evoked as scientific authorities in all four countries, there is remarkable variation in the types of organizations considered authoritative in different national contexts. We elaborate these findings by analysing ways in which politicians evaluate organizations as sources of scientific authority. While the same set of evaluative schemas are used in all four countries, each is typically applied to certain types of organization. The results suggest that both the supply of scientific policy advice and political culture shape legislators' rhetorical practices when drawing on organizations' scientific authority.

The SECURE STAIRS framework: preliminary evaluation of the impact on therapeutic climate in a secure children’s home (SCH)

PurposeThe SECURE STAIRS framework for integrated care is a trauma-informed approach to supporting staff and young people within the Children and Young People’s Secure Estate (CYPSE) in the UK. Within secure settings, therapeutic climate is a concept that encapsulates an individual’s perception of safety, connectedness with others and level of support within the environment. To support evaluation of the SECURE STAIRS framework, a Secure Children’s Home (SCH) within the North East of England examined therapeutic climate for staff and young people annually using the Essen Climate Evaluation Schema (EssenCES) over a three-year period. This paper aims to present the findings.Design/methodology/approachOver the three years, a total of 71 young people and 214 staff EssenCES questionnaires were administered. Between 2020 and 2021, the setting also experienced significant changes resulting from the COVID-19 pandemic. Numbers of young people also decreased within the setting over the three-year period.FindingsResults indicated a positive trend for therapeutic climate sub-scores. For example, Experienced Safety for young people significantly increased from 2020 to 2021. Additionally, therapeutic hold for staff was significantly higher in 2020 and 2021 in comparison to 2018.Originality/valueFindings are discussed in relation to implementation of the SECURE STAIRS framework and providing trauma-informed care for vulnerable young people within secure settings. Implications for practice are explored.

Cognitive Load and Social Media Advertising

Social media engagement requires cognitive resources, which subsequently impact the advertisements consumers see while browsing. For the most part, however, advertising practitioners and scholars still study social media ads and messages as if they will receive users’ full attention and cognition. This study has two main contributions. First, we demonstrate that the use of social media induces a measurable degree of cognitive load, presumably because the variety of networking and media components requires users to hold multiple evaluation schemas in working memory. Second, we show that while under cognitive load, participants rely on cues (e.g., number of likes and comments) that trigger heuristics to influence purchase intentions. However, product involvement was found to serve as a boundary condition wherein those under cognitive load did not rely on cues when they had higher involvement with the advertised product. Drawing on the limited capacity model of motivated mediated message processing (LC4MP), this research tests the proposed hypotheses using a pilot study and three experimental studies to provide converging evidence for the influence of social media use on heuristics and subsequent purchase intentions. Analysis of thought elicitation further supports our proposed effect of cognitive load on consumer decision making. Theoretical and practical implications are discussed.

Displacement frames: How residents perceive, explain and respond to un-homing in Black San Francisco

Few urban phenomena command as much attention as displacement. Scholars continue to refine conceptualisations of displacement to more effectively capture its diversity in forms, scales and temporalities. Recent research advocates a more inclusive conceptualisation, attuned to the processes of ‘un-homing’ – that is, the more subtle, ‘non-catastrophic’ forms of ‘slow violence’ that rupture residents’ phenomenological attachments to place and home. Advocates of the un-homing approach call on researchers to develop the data and analytical frameworks necessary for capturing the perceptions and lived experiences of displacement from the perspective of longtime residents. This article develops one such analytical framework, which we refer to as displacement frames. Building on the conceptual tools of cultural sociology, displacement frames are the evaluative schema through which residents make sense of, and act towards, the slow violence and micro-events of un-homing. Drawing on 32 interviews with long-time Black residents in San Francisco’s rapidly gentrifying Bayview Hunters Point neighbourhood, we identify three primary displacement frames: (1) displacement-by-design, (2) displacement-as-predation and (3) displacing-the-problem. As a product of residents’ historical experiences, networks and housing tenure, these frames simplify complex (and often ambiguous) experiences into a coherent narrative about the primary causes, conditions and consequences of displacement. In turn, displacement frames influence how and to what extent residents attempt to resist, prevent or perhaps even accept and support local displacement.

Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium

AbstractResearch SummaryA core question in strategy research is how firms should position themselves to gain favorable audience evaluations. Emphasizing the heterogeneity in audience predispositions, we propose that firms can gain an audience composition premium by strategically positioning themselves to gain more (less) attention from audiences with positive (negative) predispositions toward them. We argue that this approach to strategic positioning is more conducive for firms with high dispersion in their audience predispositions and that firms can increase their ability to gain an audience composition premium by engaging with audiences holding moderately diverse evaluative schemas. We employ recommender systems and topic modeling to analyze 152,312 firm‐analyst‐year observations from 1997 to 2018 and 297,931 earnings call transcripts of U.S. public firms and find strong support for our predictions.Managerial SummaryA key question managers encounter is how to increase their firms' evaluations from external evaluators such as security analysts. In this study, we show that firms can increase their aggregate analyst recommendations by influencing the composition of analysts who opt to cover them and gaining evaluations from analysts who have more favorable predispositions toward them (i.e., by gaining an audience composition premium). Our findings also suggest that gaining an audience composition premium is more important for enhancing a firm's aggregate analyst recommendations when there is a higher dispersion in analyst predispositions toward the firm. To increase its ability to gain an audience composition premium, the firm should engage with analysts who exhibit a moderate degree of heterogeneity in their evaluative schemas.

Resolving missing protein problems using functional class scoring

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples, hindering biomarker discovery and proper functional characterization. Network-based approaches can provide powerful solutions for resolving these issues. Functional Class Scoring (FCS) is one such method that uses protein complex information to recover missing proteins with weak support. However, FCS has not been evaluated on more recent proteomic technologies with higher coverage, and there is no clear way to evaluate its performance. To address these issues, we devised a more rigorous evaluation schema based on cross-verification between technical replicates and evaluated its performance on data acquired under recent Data-Independent Acquisition (DIA) technologies (viz. SWATH). Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved, confirming that FCS selects for biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p values. Despite limited cross-replicate verification rates, the predicted missing proteins as a whole have higher peptide support than non-predicted proteins. FCS also predicts missing proteins that are often lost due to weak specific peptide support.

Bridging The Gap: A Primer for Teaching About Health Disparities

By definition, geriatric psychiatrists (and other practitioners) care for patients who suffer from health care disparities, just by having a mental illness and by being an older adult. Although steps have been taken to introduce curricular content regarding health care disparities into undergraduate and graduate medical education, that content is usually limited, focusing on the role that race, ethnicity and gender have on health outcomes. Thus, the disparities brought about by having a mental illness, being an older adult and the intersection of those traits is not routinely covered. In order to better prepare our future health care providers and leaders to battle these health care disparities which are inherent in our patient population, we need to be well versed in the language of health care disparities and methods of instruction that can easily be integrated in our daily teaching activities.To this end, Drs. Nery Diaz, Paroma Mitra and Dennis Popeo offer a session that aims to address this shortcoming. Their session will begin with a brief primer on health care disparities in general (definitions, theoretical framework) and then briefly discuss the evidence base for some of the more serious disparities faced by the mentally ill, the older adult population and the intersection of these groups, as well as the intersection of the population of older adults who suffer from mental illness with race, ethnicity and gender. Next, the trio will provide a review of the literature surrounding effective measures of teaching about health care disparities, and some approaches to integrating these subjects into your established curriculum, and into your day-to-day teaching. Finally, they will introduce the general principles of equitable teaching – a framework built to address reducing disparities in the classroom - and show how this can be used to modify our existing educational and evaluative schema. There will also be time for questions and for participants to reflect on how they can incorporate what they learned into their educational practice at home.

Comprehensive Evaluation of Distribution System with High Penetration Distribution Generations Considering N-1 Security

The crisis of fossil fuel exhaustion and environmental pollution promotes the employment of distribution generators in distribution systems. The intermittent and random nature of distributed generators may bring threaten to the reliability and stability of the power grid. To improve the N-1 evaluation schema for distribution grid with DGs, an evaluation index system and comprehensive evaluation method for distribution system are proposed in this paper. Security, economy, quality and development coordination of system are analysed and used as the evaluation indexes. An evaluation framework is implemented with an analytic hierarchy process and a gray relational analysis method. The proposed index and framework were implemented tested with a modified distribution system on the CloudPSS platform. Multiple optimization objects of planning solutions for the distribution system were evaluated by the proposed frameworks. The best grid connection schema were generated based on the evaluation results. The test results demonstrate the feasibility of the proposed index system and framework.

The four different modes of psychological explanation, and their proper evaluative schemas.

I apply Benjamin's (1941) taxonomy of common scientific "modes of explanation" to the psychological context. I argue that: (i) in a "naming" mode, generalizability is not necessary; (ii) in an "analysis," generalizability is desired; (iii) in a "causal ontology," generalizability is merely one of the means to an end (theory-challenge); (iv) in a "synthesis," generalizability is (eventually) critical. A better appreciation of the diversity in psychologists' modes of explanation is crucial for cogent meta-psychological discussions.

Evaluation Schema in the Sendong Survivors’ Narratives

The conversational/everyday story is the least studied of the two primary forms of narratives: literary and conversational/everyday. Though some studies on the schemas or elements comprising conversational narratives, most of these are from Western perspectives. To address this gap, this paper determines the types and features of the evaluation schema reflected in the oral narratives of personal experiences of the typhoon Sendong (Washi) survivors in the barangays of Sta. Filomena, Hinaplanon, and Santiago of Iligan City, Philippines. Forty informants (40) were asked to describe their Sendong experiences orally, and audio-recorded interviews were transcribed. The narrative clauses were coded using Labov and Waletzky’s six narrative schema model as the main framework highlighting the evaluation schemas of these narratives. Results showed two general evaluation schemas in the Sendong narratives: the external evaluation and the embedded/internal evaluation. It is also revealed in the study that evaluation schemas can also be categorized based on content. These typologies include individual evaluation, collective evaluation (which could be inclusive or exclusive), outsider’s evaluation, and the “you” evaluation. The study concludes that the presence of these culture-specific types of evaluation (other than the individual evaluation) reflects the collectivistic culture of the Filipinos, that of focusing more on the “we” than the “I” as depicted in the evaluative comments of the Sendong survivors in this study.