Iterative Feedback Research Articles

Comparing wildlife habitat suitability models based on expert opinion with camera trap detections.

Expert knowledge is used in the development of wildlife habitat suitability models (HSMs) for management and conservation decisions. However, the consistency of such models has been questioned. Focusing on 1 method for elicitation, the analytic hierarchy process, we generated expert-based HSMs for 4 felid species: 2 forest specialists (ocelot [Leopardus pardalis] and margay [Leopardus wiedii]) and 2 habitat generalist species (Pampas cat [Leopardus colocola] and puma [Puma concolor]). Using these HSMs, species detections from camera-trap surveys, and generalized linear models, we assessed the effect of study species and expert attributes on the correspondence between expert models and camera-trap detections. We also examined whether aggregation of participant responses and iterative feedback improved model performance. We ran 160 HSMs and found that models for specialist species showed higher correspondence with camera-trap detections (AUC [area under the receiver operating characteristic curve] >0.7) than those for generalists (AUC<0.7). Model correspondence increased as participant years of experience in the study area increased, but only for the understudied generalist species, Pampas cat (β=0.024 [SE 0.007]). No other participant attribute was associated with model correspondence. Feedback and revision of models improved model correspondence, and aggregating judgments across multiple participants improved correspondence only for specialist species. The average correspondence of aggregated judgments increased as group size increased but leveled off after 5 experts for all species. Our results suggest that correspondence between expert models and empirical surveys increases as habitat specialization increases. We encourage inclusion of participants knowledgeable of the study area and model validation for expert-based modeling of understudied and generalist species.

The Development and Integration of a Safety Officer Role to Facilitate Prevention of COVID-19 Virus Transmission in an Adult Inpatient Rehabilitation Setting Using Collaborative Change Leadership Methodology.

With the onset of the COVID-19 pandemic, a large urban academic hospital responded by creating the temporary role of a "Safety Officer (SO)." The key task of the SO role was to supervise staff donning and doffing personal protective equipment (PPE) and provide real-time feedback on their performance. The support for safe donning and doffing would contribute to staff well-being by reducing their fear of infection transmission. A Collaborative Change Leadership (CCL) approach was used to facilitate the development, implementation, and evaluation of the role. This included an iterative feedback process with clinicians and safety officers to continually refine the role. Feedback indicated value in the initiative as increasing staff confidence about preventing virus transmission, as well as their sense of safety at work. Areas for future improvement included additional communication strategies for interprofessional teams and external partners, as well as planning around logistics to better support the safety officers in performing this new, temporary role. The Safety Officer role was able to help alleviate concerns regarding potential infection transmission and contribute positively to staff well-being.

The Use, Effectiveness, and Impact of Workplace-Based Assessments on Teaching, Supervision and Feedback Across Surgical Specialties

To investigate the use and effectiveness of Workplace-based assessments (WBAs) and their impact on training, feedback, and perioperative teaching in surgical training programs. A mixed methods cross-sectional, national electronic survey was conducted with surgical trainees and consultant trainers. The trainees and supervising faculty were from all 8 major surgical training universities across 11 surgical disciplines in South Africa. A total of 108 surgical trainees and 41 supervising consultant trainers from 11 surgical disciplines across 8 surgical training universities responded to the survey. The most significant educational gap identified by both the surgical trainees and trainers across all surgical disciplines was inadequate perioperative feedback. A third of the respondents were currently using workplace-based assessments. The WBA users (both trainees and trainers) had a higher rating for the general quality of surgical feedback than WBA nonusers (p = 0.02). WBA users also had a higher rating for the general quality of feedback given to trainees on their skills and competence (p = 0.04) and a higher rating for trainee supervision (p = 0.01) and the specialist training program overall (p = 0.01). The WBA users also had a higher rating for the assessment of competencies such as the trainee as an effective communicator (p < 0.01) and collaborator (p = 0.04). This study found that the use of WBAs enhances the quality and effectiveness of feedback in surgical training programs. We also found that the use of WBAs enhance perioperative teaching and learning and improves the assessment of relational competencies. This was also associated with high ratings for the quality of trainee supervision. Faculty and trainee development, strengthening the trainee-trainer relationship, and integrating iterative stakeholder feedback could help realize the full potential of WBAs to augment surgical training across disciplines.

Updated consensus guidelines on the management of Phelan-McDermid syndrome.

Phelan-McDermid syndrome (PMS) is a genetic condition caused by SHANK3 haploinsufficiency and characterized by a wide range of neurodevelopmental and systemic manifestations. The first practice parameters for assessment and monitoring in individuals with PMS were published in 2014; recently, knowledge about PMS has grown significantly based on data from longitudinal phenotyping studies and large-scale genotype-phenotype investigations. The objective of these updated clinical management guidelines was to: (1) reflect the latest in knowledge in PMS and (2) provide guidance for clinicians, researchers, and the general community. A taskforce was established with clinical experts in PMS and representatives from the parent community. Experts joined subgroups based on their areas of specialty, including genetics, neurology, neurodevelopment, gastroenterology, primary care, physiatry, nephrology, endocrinology, cardiology, gynecology, and dentistry. Taskforce members convened regularly between 2021 and 2022 and produced specialty-specific guidelines based on iterative feedback and discussion. Taskforce leaders then established consensus within their respective specialty group and harmonized the guidelines. The knowledge gained over the past decade allows for improved guidelines to assess and monitor individuals with PMS. Since there is limited evidence specific to PMS, intervention mostly follows general guidelines for treating individuals with developmental disorders. Significant evidence has been amassed to guide the management of comorbid neuropsychiatric conditions in PMS, albeit mainly from caregiver report and the experience of clinical experts. These updated consensus guidelines on the management of PMS represent an advance for the field and will improve care in the community. Several areas for future research are also highlighted and will contribute to subsequent updates with more refined and specific recommendations as new knowledge accumulates.

Optimizing Informed Consent Discussions: Developing a Narrative for Transfusion Consent.

Ensuring patient informed consent is a key tenet of modern medicine. Although transfusion of blood products is among the most common medical procedures performed in hospitalized patients, there is evidence that informed consent for transfusion is at times incomplete, poorly understood, hurried, and/or inaccurate. This study aimed to develop a narrative that can be used as a framework for practicing physicians and for educational purposes to optimize the process for obtaining informed consent for blood transfusion. The narrative was developed using a modified Delphi approach with 5 Rounds that included feedback from transfusion medicine (TM) experts, transfusion-provider physicians, and lay people. The surveys collected qualitative and quantitative data analyzed using thematic content analysis and descriptive statistics, respectively. Results from Rounds 1 and 2 generated a draft narrative and Rounds 3 to 5 informed further modifications. Round 1 included draft narrative scripts from 28 TM experts; thematic coding generated 97 topics. In round 2, 22/28 of the initial experts rated items identified from Round 1. Those with a content validity index (CVI) ≥ 0.8 were used by the authors to develop a narrative. In Round 3, 20/24 participants from Round 2 reviewed the narrative with 100% agreeing on the items included and 90% agreeing the flow was logical. In Round 4, 23 transfusion prescribers (non-TM physicians) reviewed the narrative for flow, manner, length, and usability; there was 83% agreement with the nonexclusion of important topics; 91% felt it would be effective for teaching trainees. Round 5 included 24 nonmedical laypeople of different demographics. Most participants (92%) thought that the script was appropriate in length and there were opportunities to ask questions. Participants could also identify the adverse transfusion reactions and understand that they could refuse the transfusion. A narrative for obtaining informed consent for blood transfusion was created through multiple rigorous iterations of review and feedback with both transfusion providers and the lay public. The narrative, developed for a specific clinical scenario, was well-received by medical and nonmedical participants and can be used, and modified, to help ensure patients understand the risks and benefits of blood transfusion.

High-Resolution Iterative Feedback Network for Camouflaged Object Detection

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. To design better feedback feature flow and avoid the feature corruption caused by recurrent path, an iterative feedback strategy is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our HitNet breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. In addition, to address the data scarcity in camouflaged scenarios, we provide an application example to convert the salient objects to camouflaged objects, thereby generating more camouflaged training samples from the diverse salient object datasets. Code will be made publicly available.

An international, prospective observational study on traumatic brain injury epidemiology study protocol: GEO-TBI: Incidence.

The epidemiology of traumatic brain injury (TBI) is unclear - it is estimated to affect 27-69 million individuals yearly with the bulk of the TBI burden in low-to-middle income countries (LMICs). Research has highlighted significant between-hospital variability in TBI outcomes following emergency surgery, but the overall incidence and epidemiology of TBI remains unclear. To address this need, we established the Global Epidemiology and Outcomes following Traumatic Brain Injury (GEO-TBI) registry, enabling recording of all TBI cases requiring admission irrespective of surgical treatment. The GEO-TBI: Incidence study aims to describe TBI epidemiology and outcomes according to development indices, and to highlight best practices to facilitate further comparative research. Multi-centre, international, registry-based, prospective cohort study. Any unit managing TBI and participating in the GEO-TBI registry will be eligible to join the study. Each unit will select a 90-day study period. All TBI patients meeting the registry inclusion criteria (neurosurgical/ICU admission or neurosurgical operation) during the selected study period will be included in the GEO-TBI: Incidence. All units will form a study team, that will gain local approval, identify eligible patients and input data. Data will be collected via the secure registry platform and validated after collection. Identifiers may be collected if required for local utility in accordance with the GEO-TBI protocol. Data related to initial presentation, interventions and short-term outcomes will be collected in line with the GEO-TBI core dataset, developed following consensus from an iterative survey and feedback process. Patient demographics, injury details, timing and nature of interventions and post-injury care will be collected alongside associated complications. The primary outcome measures for the study will be the Glasgow Outcome at Discharge Scale (GODS) and 14-day mortality. Secondary outcome measures will be mortality and extended Glasgow Outcome Scale (GOSE) at the most recent follow-up timepoint.

FmLRE: A Low-Resource Relation Extraction Model Based on Feature Mapping Similarity Calculation

Low-resource relation extraction (LRE) aims to extract relations from limited labeled corpora. Existing work takes advantages of self-training or distant supervision to expand the limited labeled data in the data-driven approaches, while the selection bias of pseudo labels may cause the error accumulation in subsequent relation classification. To address this issue, this paper proposes fmLRE, an iterative feedback method based on feature mapping similarity calculation to improve the accuracy of pseudo labels. First, it calculates the similarities between pseudo-label and real-label data of the same category in a feature mapping space based on semantic features of labeled dataset after feature projection. Then, it fine-tunes initial model according to the iterative process of reinforcement learning. Finally, the similarity is used as a threshold for screening high-precision pseudo-labels and the basis for setting different rewards, which also acts as a penalty term for the loss function of relation classifier. Experimental results demonstrate that fmLRE achieves the state-of-the-art performance compared with strong baselines on two public datasets.

Surface-Related Multiples Elimination for Waterborne GPR Data

Ground-penetrating radar (GPR) is a well-respected, effective, and efficient geophysical technique. However, for underwater engineering detection and underwater archaeology, the measured B-scan profiles typically contain surface-related multiple waves, which can reduce the signal to noise ratio and interfere with the interpretation of results. SRME is a feedback iteration method based on wave equation, which is frequently utilized in marine seismic explorations but very rarely in GPR underwater engineering detection. To fill this gap, we applied SRME to suppress multiples that appear in GPR underwater images. When we compared the effectiveness of the underwater horizontal layered model and the underwater undulating interface model, we found a high match rate between the predicted and the real-world multiples. In addition, the addition of the Gaussian random noise level with a 4% maximum amplitude to the B-scan profile of the horizontal stratified model yielded satisfactory multiple suppression results. Finally, we applied this method to the B-scan GPR section of actual underwater archaeological images to achieve multiple suppression, which can more effectively weaken and inhibit the surface-related multiples. Both numerical simulations and actual field data show that the SRME method is highly suitable for interpreting waterborne GPR data, and more accurate interpretation can be obtained from the GPR profile after multiples suppression.

Low complexity nonlinear detection for multiuser hybrid MIMO systems and performance analysis

This paper proposes an analytical framework and a system of distortion-aware receiver structures for single-carrier uplink hybrid beamforming-based multiple-input-multiple-output (MIMO) channels. Proposed single-user processing units cooperate at the base station (BS) in an iterative manner for multi-user symbol detection under hardware impairments. Firstly, in the proposed system, an iterative nonlinear interference cancellation (nIC) based on feedforward neural network (NN) modeling is developed to eliminate the nonlinear distortion effects of power amplifiers (PAs). Furthermore, depending on the fading pattern of the instantaneous channel, it is analytically shown that a large gain may be applied to the distortion signal during the equalization stage, which yields distortion amplification causing a significant decrease in the signal-to-distortion-plus-noise ratio (SDNR). A reduced complexity distortion aware detector, based on a bank of fractionally delayed iterative block decision feedback equalization (IB-DFE) receivers, is introduced to adjust the combiner weights for fractionally delayed IB-DFE outputs so that distortion power is minimized before detection. Consequently, multi-user detection capability is enhanced considerably. Secondly, an asymptotic SDNR analysis is performed. The theoretical analysis, verified via numerical evaluations, clearly shows that distortion amplification limits the achievable SDNR at the BS, and the proposed reduced complexity multi-user receiver architecture improves the BER performance by effectively mitigating this effect.

Enhancing pre-employment transition services: A type 1 hybrid randomized controlled trial protocol for evaluating WorkChat: A Virtual Workday among autistic transition-age youth

Autistic transition-age youth experience high rates of unemployment and underemployment, in part due to the social challenges they may face when having conversations in the workplace. In an effort to help enhance conversational abilities in the workplace, our collaborative team partnered to develop WorkChat: A Virtual Workday. Specifically, our team of scientists, community partners, and diversity and inclusion experts participated in a community-engaged process to develop WorkChat using iterative feedback from autistic transition-age youth and their teachers. With initial development complete, this study reports on the protocol that our collaborative team developed, reviewed, and approved to conduct a randomized controlled trial (RCT) to evaluate the real-world effectiveness and initial implementation process outcomes of WorkChat when integrated into post-secondary pre-employment transition services (Pre-ETS). Our aims are to: 1) evaluate whether services-as-usual in combination with WorkChat, compared to services-as-usual with an attention control, enhances social cognition and work-based social ability (between pre- and post-test); reduces anxiety about work-based social encounters (between pre- and post-test), and increases sustained employment by 9-month follow-up; 2) evaluate whether social cognitive ability and work-based social ability mediate the effect of WorkChat on sustained employment; and 3) conduct a multilevel, mixed-method process evaluation of WorkChat implementation.

Consensus on Criteria for Selection of Sign Language Mobile Apps: A Delphi Study

In the rapidly evolving digital learning landscape, sign language mobile apps are vital in advancing sign language teaching. However, ensuring the quality of these apps remains a critical challenge. To address this gap, this study employs the fuzzy Delphi technique to establish a robust set of criteria for evaluating the quality of sign language mobile apps. By leveraging the collective wisdom and expertise of a panel of experts, the fuzzy Delphi technique facilitates a structured process for achieving consensus on the essential factors contributing to evaluating sign language mobile apps. Through rigorous rounds of iterative feedback and analysis, the study identifies a comprehensive list of reliable criteria encompassing various dimensions, including functionality, usability, accessibility, and pedagogical effectiveness. The criteria established through this method serve as a valuable resource for developers, educators, and clients in selecting and developing top-notch sign language mobile apps. Developers can use the criteria as a guide during the design and development stages, ensuring that their apps meet the highest quality and user experience standards. Educators can rely on the criteria as a checklist for evaluating and selecting appropriate apps that align with their teaching objectives and cater to the diverse learning needs of their students. Clients, such as educational institutions or individuals seeking sign language learning resources, can make informed decisions by referring to the established criteria, promoting the adoption of clear and impactful sign language mobile apps. This study emphasizes the significance of applying the fuzzy Delphi method in the context of sign language mobile app assessment. Involving experts from relevant fields ensures that the established criteria capture the multifaceted nature of compelling sign language learning experiences. Developing a comprehensive and reliable set of criteria contributes to improving existing apps and encourages innovation in creating new apps that better serve the needs of sign language learners. Overall, this research extends the knowledge base of sign language teaching in the digital age by providing a robust framework for assessing the quality of sign language mobile apps. The findings of this study empower stakeholders in the education and technology sectors to make informed decisions, fostering the advancement of sign language teaching and promoting inclusivity in digital learning environments.

Implementation Lessons Learned: Distress Behaviors in Dementia Intervention in Veterans Health Administration.

Evidence-based practices to manage distress behaviors in dementia (DBD) are not consistently implemented despite demonstrated effectiveness. The Veterans Health Administration (VA) trained teams to implement Staff Training in Assisted Living Residences (STAR)-VA, an intervention to manage DBD in VA nursing home settings, or Community Living Centers (CLCs). This paper summarizes multiyear formative evaluation results including challenges, adaptations, and lessons learned to support sustained integration into usual care across CLCs nationwide. STAR was selected as an evidence-based practice for DBD, adapted for and piloted in VA (STAR-VA), and implemented through a train-the-trainer program from 2013 to 2018. Training and consultation were provided to 92 CLC teams. Evaluation before and after training and consultation included descriptive statistics of measures of clinical impact and survey feedback from site teams regarding self-confidence, engagement, resource quality, and content analysis of implementation facilitators and challenges. STAR-VA training and consultation increased staff confidence and resulted in significant decreases in DBD, depression, anxiety, and agitation for Veterans engaged in the intervention. Implementation outcomes demonstrated feasibility and identified facilitators and barriers. Key findings were interpreted using implementation frameworks and informed subsequent modifications to sustain implementation. STAR-VA successfully prepared teams to manage DBD and resulted in improved outcomes. Lessons learned include importance of behavioral health-nursing partnerships, continuous engagement, iterative feedback and adaptations, and sustainment planning. Evaluation of sustainment factors has informed selection of implementation strategies to address sustainment barriers. Lessons learned have implications for integrating team-based practices into system-level practice.

Information sharing in differences of sex development: The creation of a caregiver-support tool.

Social support can be a protective factor against the negative mental health outcomes experienced by some parents and caregivers of children with differences of sex development (DSD). However, established social support networks can be difficult to access due to caregiver hesitancy to share information with others about their child's diagnosis. Health care providers in the field of DSD, and particularly behavioral health providers, are well positioned to help caregivers share information with the important people in their lives in order to access needed social support. This article summarizes the development of a clinical tool to help clinicians facilitate discussions regarding information sharing with caregivers of children with DSD. Members of the psychosocial workgroup for the DSD -Translational Research Network completed a survey about their experiences facilitating information sharing discussions with caregivers of children with DSD and other health populations. The results of this survey were used to develop a clinical tool using ongoing iterative feedback from workgroup members, based on principles of user-centered design and quality improvement. Workgroup members consider information sharing an important aspect of working with caregivers of children with DSD. Additional resources and tools were identified as potentially helpful to these discussions. The DSD Sharing Health Information Powerfully-Team Version (SHIP-T) is a resource tool for DSD health care team members to utilize in hospital and ambulatory settings to help caregivers of children with DSD share information with their social support networks. The final SHIP-T is included in this article. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Real Time Chatbot Using Python

Abstract: Real-time chatbots developed using Python have emerged as powerful tools for enhancing customer support, improving user experiences, and streamlining business processes. Leveraging Python's extensive libraries and frameworks, such as NLTK, Flask, and telebot, developers can build intelligent and scalable chatbot systems.This paper provides an overview of the key components and techniques involved in developing a real-time chatbot using Python. It explores the process of requirement gathering, use case definition, conversation flow design, and performance optimization. Integration with backend services, error handling, validation, and user experience (UX) design are also discussed. The utilization of natural language understanding (NLU) algorithms and techniques allows chatbots to interpret and comprehend user intents, providing accurate and context-aware responses. Integration with REST APIs, Flask, and telebot facilitates seamless communication and interaction between the chatbot and users. Furthermore, the paper highlights the importance of security, privacy, and ethical considerations in chatbot systems. It emphasizes the significance of continuous testing, feedback iteration, and user-centric design principles to refine the chatbot's performance and enhance the user experience. Looking ahead, future work in real-time chatbot development using Python includes advancements in natural language processing (NLP), personalized user experiences, multi-modal capabilities, and the integration of voice assistants. Ethical considerations and explainable AI techniques will also be critical for building trustworthy and responsible chatbot systems. In conclusion, real-time chatbots developed using Python offer immense potential for transforming customer support, automating processes, and delivering personalized and efficient services. With ongoing advancements in NLP, AI, and user interface design, the future of real-time chatbots holds exciting possibilities for enhanced user interactions and seamless automation.

Using the Person-Based Approach to Develop a Digital Intervention Targeting Diet and Physical Activity in Pregnancy: Development Study.

In pregnancy, eating well, keeping active, and avoiding excessive weight gain are associated with better maternal and fetal health outcomes. Dietary and physical activity (PA) interventions can be effective in changing behaviors and managing weight gain. The comparatively lower cost and greater accessibility of digital interventions make them an attractive alternative to in-person interventions. Baby Buddy is a free pregnancy and parenting app from the charity Best Beginnings. Designed to support parents, improve health outcomes, and reduce inequalities, the app is actively used within the UK National Health Service. It offers an ideal platform for delivering and evaluating a new prenatal dietary and PA intervention. The aim of this study was to create a theory-based intervention within Baby Buddy to empower, encourage, and support expectant parents to develop healthier dietary and PA habits for pregnancy and parenthood. The intervention's development process was guided by the Behavior Change Wheel, with the person-based approach used to create and test its design. Three stages of qualitative research with pregnant and recently pregnant parents guided the intervention design. Study 1 (n=30), comprising 4 web-based focus groups and 12 telephone interviews, gauged response to the rudimentary concept and generated ideas for its development. Results were analyzed thematically. At this stage, the guiding principles for the intervention development were established, and regular team meetings ensured that the intervention design remained aligned with Best Beginnings' objectives, evidence-based approach, and feasibility criteria. Study 2 (n=29), comprising web-based individual and couple interviews, explored design ideas using wireframes and scripts and generated iterative feedback on the intervention content, branding, and tone. A table of changes analysis tracked design amendments. Study 3 (n=19) tested an app prototype using think-aloud interviews with current Baby Buddy users. A patient and public involvement and engagement activity (n=18) and other expert contributors (n=14) provided ad hoc input into the research process and design development. Study 1 confirmed the appeal and relevance of the intervention concept and its novel approach of including partners. The identified themes underpinned the development of the intervention design. Iterative feedback from study 2, in conjunction with patient and public involvement and engagement and expert contributor input, helped refine the intervention design and ensure its relevance and appeal to a diverse target user group. Study 3 highlighted functionality, content, and design issues with the app prototype and identified ways of improving the user experience. This study illustrates the value of combining a theoretical method for intervention development with the person-based approach to create a theory-based intervention that is also user-friendly, appealing, and engaging for its target audience. Further research is needed to evaluate the effectiveness of the intervention in improving diet, PA, and weight management in pregnancy.

EFL student engagement with giving peer feedback in academic writing: A longitudinal study

While a number of studies on peer feedback in academic writing have been done on how students engaged with received feedback in a single assignment, little work has examined engagement with giving feedback and across a longer timeline, which is problematic for a pedagogical technique that is often new to EFL students and should be used repeatedly in a semester. Using a mixed method design that draws upon transcripts of voiced peer comments, monthly reflection journals, semi-structured interviews, stimulated recalls, and two rounds of a self-efficacy survey, this longitudinal study examined how three EFL undergraduates of relatively different proficiency levels affectively, behaviorally, and cognitively engaged with giving feedback over ten iterative feedback practices in an EAP writing course. The findings showed both initial engagement differences and extensive gains in engagement across all three learners despite challenges in proficiency and self-efficacy. Pedagogical implications of how peer feedback could be enhanced in EAP writing courses are discussed.

Development of a character-strengths based coaching program for rural community health workers to address their work stress in Madhya Pradesh, India

While rural ‘Accredited Social Health Activists (ASHAs)’ delivering primary care at the village-level in India experience high levels of work stress and burnout, little is known about the potential of positive psychology interventions in helping them respond to stressful situations. We aim to describe a systematic approach to designing and developing a ‘character-strengths based’ coaching program for rural ASHAs to help reduce their work-stress and improve their delivery of routine primary care. The development of the coaching program involved: (1) formative work, (2) blueprint development, (3) content development, (4) content-testing, and focus groups discussions to evaluate the feasibility and acceptability of the intervention, specifically the coaching workshop. This was followed by thematic qualitative analysis of ASHA perspectives/feedback to inform further modifications to the workshop. Intervention development occurred over 11 months, and the final coaching material consisted of a ‘content manual’ (for ASHAs) with four modules including character-strengths based ‘strategies’ to address challenges/stressors arising at health facilities, village communities and homes. Coaching material also included a workshop ‘facilitator’s manual’ having session-wise detailed instructions, a list of ‘energizers’ and plans for the coaching workshop. Consistent efforts were made to tailor the case-examples and workshop activities (e.g., roleplays, videos and reflections or discussion-based activities) to the local culture and context. This study illustrates a step-wise approach to contextually adapt the evidence-based character-strengths intervention approaches with iterative feedback from stakeholders (ASHAs), to develop a face-to-face coaching program aiming to reduce work stress, tailored to the context of a rural low-resource setting.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

Research Priorities to Support Women Veterans' Reproductive Health and Health Care Within a Learning Health Care System.

D151. The Gender-Q: A Rigorous, Modular, Patient-reported Outcome Measure For Gender-affirming Care

PURPOSE: To develop a comprehensive, rigorous PROM to assess outcomes of gender-affirming care in clinical practice, research, and quality initiatives (the GENDER-Q) METHODS: A mixed-methods, multiphase approach was used. In phase 1, in-depth interviews were conducted with adults seeking gender-affirming care from 4 countries (US, Canada, Denmark, Netherlands). Data were analyzed and used to develop a conceptual framework and item pool, which was used to develop preliminary scales. Iterative feedback was sought from clinicians and patients on the scales, and used to refine/develop new scales. The scales were pilot-tested using a crowd-sourcing platform. RESULTS: Data from interviews with 84 transgender and gender diverse adults (34±14years) resulted in 42 preliminary scales that were developed covering the concepts in the conceptual framework. Feedback from 50 clinicians and patient participants (n=7 to 14, depending on scale) was used to refine and develop 15 new scales, resulting in 55 scales in the field test version. Pilot-test was completed (n=602) and used to make minor changes to the GENDER-Q (e.g., drop items). CONCLUSION: The GENDER-Q was developed using extensive input from TGD individuals and clinician experts and represents the most comprehensive set of independently functioning, unidimensional scales that are available to-date. Phase 2 involves an international field-test (currently underway; n=1900), following which the scales will be made available at no charge for academic and clinical research.