Objectives: The full-scale Russian invasion of Ukraine resulted in a refugee crisis. The terms of employment of Ukrainian Refugee background Medical Professionals (UKR-MPs) in the Polish healthcare system were liberalised. The aim of the study was to identify challenges in job seeking and the integration of Ukrainian war refugee healthcare workers into the Polish healthcare system.Methods: A qualitative, descriptive study based on content thematic analysis of Facebook content. We analyzed 1,700 posts published on two public Facebook groups intended for UKR-MPs.Results: The most common problems encountered by UKR-MPs were: 1) lack of easy-to-understand information about the list of documents necessary to apply for a work permit, 2) lack of feedback from those responsible for handling individual cases, and 3) long waiting time for the decision issued by the Ministry of Health.Conclusion: Despite the promptly implemented solutions enabling access to the job market by UKR-MPs, the refugees have encountered considerable administrative difficulties, as well as those arising from insufficient knowledge of the regulations on working as medical professionals in Poland under the EU law. The full-scale Russian invasion of Ukraine resulted in a refugee crisis, with the number of refugees exceeding 8 million people  and as rUnder the domestic and international law in force in Poland, a refugee needs to be granted refugee status under the 1951 Refugee Convention, and hold a refugee travel document .In the EU member states, the right to practice as a doctor or nurse is strictly regulated and has so far only been issued to individuals who 1) graduated from the respective studies or 2) had their diploma recognized in one of the EU countries.Following Russia’s full-scale invasion of Ukraine in February 2022, the requirements for obtaining by UKR-MPs the right to practice medicine or nursing in Poland have been liberalized . In PolaDespite the simplified work permit procedure; UKR-MPs may still face various challenges due to substantial differences in educational standards and their unawareness of varying standards of practice in Ukraine and in the EU member states. Insufficient knowledge of the Polish language may also constitute a major obstacle. To identify the challenges faced by UKR-MPs, arriving in Poland as a result of the refugee crisis and planning to work as doctors or nurses, we categorised the content created by Facebook users , 10.The study was based on a well-documented theoretical framework. Over the past decade, an expanding literature has explored the ways in which refugees rely on mobile communication technologies and social media content to stay in touch with a wider community and to access relevant information and services in their new places . There iThe aim of the study was to identify challenges in job seeking and the integration of Ukrainian war refugee healthcare workers into the Polish healthcare system by categorising the content created by Facebook users.A qualitative, descriptive study based on Facebook content thematic analysis.The qualitative method used involves the process of perception, interpretation and conceptualisation of the meaning of qualitative data.The criteria to determine rigour or trustworthiness in a qualitative study are credibility, dependability, conformability and transferability. Qualitative content analysis was performed according to Graneheim & Lundman  and instThe selection of thematic groups on Facebook was driven by their accessibility (public access) and the thematic focus of the group.We focused on posts published on two, large public Facebook groups: “Doctors, Nurses from Ukraine - work”  , both laThese groups were selected for analysis as they are the only public groups available on Facebook aimed at particular users - doctors and nurses from Ukraine, arriving in Poland after 24 February 2022.The Python programming language was used to access the Facebook Scraper library , throughThe final dataset comprised a total of 3,038 user messages and the first stage of content analysis included categorizing data into: 1) Posts (835 items), 2) Comments on posts  and 3) Responses to comments on posts . n = 65). Subsequently, data were deleted that did not constitute substantive content: SPAM, off-topic advertisements, posts characteristic of correspondence between members of social media newsgroups, e.g., thank you very much for your help, hello, I replied in a private message, priv, contact me, greetings to new members . Thus, a total of 1,329 records were removed from the database. Later, the database of the remaining 1,709 machine translated records was proofread by a Ukrainian-Polish translator  lack of easy-to-understand information about the list of documents necessary to apply for a work permit, 2) lack of feedback from those responsible for handling individual cases, and 3) long waiting time for the decision issued by the MH.As for the list of required documents, it should be noted that all the necessary information, updated on a regular basis, is available in Ukrainian on the MH website. Yet, for many years not Ukrainian but Russian had been the official language in Ukraine. It was only in 2017 that the use of Ukrainian became mandatory in most state institutions; nonetheless, a high percentage of Ukrainians have continued speaking Russian on a daily basis. This was particularly true in eastern Ukraine, and it was from this part of the country that the first wave of refugees residing in Poland arrived. This factor might have affected UKR-MPs ability to understand information published in Ukrainian on the MH website. Also, document templates are now available online that were not yet available in early 2022.Content analysis also suggests that many applications were submitted by refugees not eligible to work as doctors or nurses in Poland but highly motivated to do so, e.g., applying for the position of a nurse with a feldsher’s degree obtained in Ukraine or applying for the position of an ultrasound specialist despite the fact that such a specialisation does not exist in Poland.The refugees’ knowledge about the Polish educational system and the work setting was by far insufficient, which may have constituted an objective difficulty when trying to undertake work. In Ukraine, there still exists the profession of a feldsher, which may be acquired by graduating from a nursing secondary school or college of further education . In PolaPoland implemented the Bologna Process standards, and with the accession to the EU in 2004, it also adopted the European Parliament and Council Directive 2005/36/EC, which regulates the recognition of professional qualifications for all EU member states. In accordance with it, the education system for the regulated professions, i.e., the professions of doctor and nurse, should be uniform and enable citizens to be mobile and take up work in any of the EU member states . As UkraBridging programmes for Polish nurses were just one example of the implementation of EU requirements. In the years 2008–2015, bridging courses were implemented from the European Social Fund for persons working in the nursing profession, but with only a secondary education, in order to supplement their qualifications in accordance with EU requirements. These programmes were successfully completed by over 37,000 nurses in Poland . ConcurrThe analysed Facebook posts showed a lack of UKR-MP knowledge of the existing discrepancies or a lack of awareness that such discrepancies exist. The refugees had neither been planning to change their work setting, nor had they been interested in the regulations in other countries. This unawareness may have caused UKR-MPs with a degree in pharmacy or that of a feldsher to apply for work as nurses, indicating that they had worked in this profession in Ukraine for many years. This hypothesis may also be indirectly confirmed by the data obtained from the MH Department for Medical Personnel Development. As for the group of nurses, by 12 September 2022, 618 applications had been submitted, of which 456 were approved (73%). In the following 3 months, until December 2022, the number of applications submitted amounted to 1,190, of which 761 were approved (63%). Fewer approvals probably resulted from an increasing number of applications submitted by refugees not eligible to work in the Polish healthcare system. The exact reasons for the refusals would require an in-depth analysis of the documentation.Among UKR-MPs, the Polish language proficiency also poses a significant challenge, even though this aspect has been greatly liberalised. Prior to the 2022 armed conflict, the knowledge of the Polish language was a key criterion in the assessment of a foreigner entering the medical profession. The simplified work permit procedure no longer requires a language certificate, and the responsibility for employing a person with language proficiency appropriate for the job lies with the employer. This has raised a great deal of controversy among Polish nurses . In viewThe analysis showed UKR-MP concerns regarding the level of proficiency in spoken and written Polish required by Polish employers. Refugees pointed to the fact that a Polish language certificate is required of doctors but this prerequisite does not apply to nurses. The findings of other researchers show that the knowledge of the language of the host country is the most important aspect of successful professional adaptation for refugees who wish to take up employment and it enables them to adapt more quickly to living in a country where they take up employment , 36.Insufficient feedback from the MH on the status of one’s application was yet another issue raised by UKR-MPs, e.g., 1) difficulty contacting the MH Department for Medical Personnel Development, responsible for issuing work permits, either by e-mail or over the phone; 2) long waiting time for a final decision (around 6 months or longer); 3) lack of information on factors affecting the time taken to process individual applications; 4) an ineffective system of requests to supplement necessary documents. Furthermore, UKR-MPs did not receive information from the MH on the further steps required to obtain a work permit or the necessity to report on this matter to the District Chamber of Nurses and Midwives or the District Chamber of Physicians.The occurrence of the aforementioned barriers may have stemmed both from factors attributable to the ineffective work of the public administration in Poland, as well as from a lack of experience in handling official matters in the host country by UKR-MPs. Despite the substantial changes implemented over the past year in terms of informing the UKR-MPs about the required documents, the implementation by the MH of an electronic document processing system should be undertaken to further facilitate this process. The MH is based in Warsaw and UKR-MPs are required to submit paper versions of documents. As a result, UKR-MPs would often submit documents by post, as they reside in remote parts of Poland. The need to process the documents without the support of an electronic system would certainly cause delays in processing an increasing number of applications. In addition, a shortage of administrative staff has been observed in the Polish healthcare system for several years . The MH It now seems indispensable to make widely available comprehensive, practical information about undergraduate and postgraduate education and the requirements for taking up employment in the healthcare system in Poland. In our opinion, the group of healthcare beneficiaries is highly diverse, so such information could be made available by medical universities educating doctors and nurses, professional self-government bodies, or the Medical Centre for Postgraduate Education responsible for postgraduate education of medical professionals in Poland. These diverse entities, responsible for different stages of the preparation of medical personnel for work in Poland may cooperate to prepare comprehensive information on the functioning of the medical and nursing professions in Poland, available to UKR-MPs, given the fact that this group will be increasingly numerous.Only a small number of the posts discussed courses developing professional or language competences, information on the possibility of studying in Poland, or assistance with daily activities of refugees in Poland. Insufficient knowledge of the Polish language preventing participation in training courses may be the most important factor behind this phenomenon. Furthermore, the vast majority of the refugees declare their willingness to return to Ukraine after the war . NonetheAlso, it is important to add that war refugees, being one of the most vulnerable groups, are at risk of experiencing negative mental health outcomes such as posttraumatic stress disorder, anxiety, and depression -42. ThisThese conditions may be further exacerbated by a lack of social support due to language barriers and severed social connections with relatives, friends, and colleagues. These circumstances significantly hinder their ability to adapt to new situations and acquire new skills, knowledge and ability to cope with new challenges and obstacles while job seeking in the host country .This first study to address the issue of obtaining work permits and taking up employment by UKR-MPs in Poland may serve as a valuable source of information on obstacles to access to the healthcare job market. It may form the basis for immediate practical corrective measures. The analysis of content created by UKR-MPs constitutes research material of particular significance. The analysis covers a year of observation, thus it reflects the changes and evolution of the described phenomena over time. The freedom of expression in social media in absence of the investigator, helps to obtain the users’ genuine opinions.One significant limitation of the research results presented in this paper is the inability to provide an accurate socio-demographic description of the study group. This limitation arises from the fact that current technical restrictions on data retrieval from Facebook prevent obtaining socio-demographic information, following the data breach conflict between Facebook and Cambridge Analytica Data, c.f. a paper by Tommaso Venturini & Richard Rogers (2019) . Though The analysis of UKR-MPs needs as regards the functioning of the healthcare system in Poland and forms of the most effective and cost-efficient provision of further education, as expected by this group.Despite the promptly implemented administrative solutions enabling access to the job market by UKR-MPs, the refugees have encountered considerable administrative difficulties, as well as those arising from insufficient knowledge of the regulations on working as doctors and nurses in Poland under the EU law. The presented research also identifies the most urgent needs of UKR-MPs as regards supplementing their knowledge on the functioning of the healthcare system in Poland. Addressing these needs as quickly as possible may significantly affect the UKR-MP preparedness for work in Polish healthcare institutions, which will be beneficial for everyone.


The adoption of Robotic Assisted Surgery (RAS) has grown around the world. This is also the case in the Middle East and Gulf region and specifically to the United Arab Emirates (UAE). The perception of RAS has been studied in the USA, Europe, and Canada. However, there is limited research on the perception of RAS in the UAE. The study aims to examine the perception of RAS among healthcare experts in the UAE and potential challenges. This qualitative study is based on interviewing healthcare experts in the UAE. Most of the study participants were clinicians and surgeons. In the UAE, RAS is adopted in general surgery, urology, brain surgery, and obstetrics and gynecology. Our findings show that healthcare experts have positive perceptions of RAS. The cost and lack of RAS training program are considered as challenges to adopting RAS in healthcare practices. More research is encouraged to examine perception variations with surgical practices in the UAE, Gulf and the Middle East. Robotic Assisted Surgery (RSA) can be defined as a procedure using small instruments that are mounted on a structure such as a robotic platform and is controlled by a surgeon operating a computer-controlled interface .The use of RAS has grown significantly around the world . For insRobotic surgery advanced with second generation surgical robots such as the Imperial College London’s PROBOT which has soft-tissue surgical capabilities. In 1998, Zeus surgical robot was introduced followed by the Da Vinci system. The Zeus system was used to perform the first beating-heart coronary operation, and in 2001, it was used to perform a surgical operation with the code Lindbergh operation. Like the Lindberg flight across the Atlantic Ocean, it was the first trans-Atlantic operation performed using a tele-robotic system, with a robotic surgical device by surgeons in the USA and a patient in France. The market consolidated with the acquisition of Da Vinci by the parent company of Zeus. Intuitive Surgical. It marketed commercial surgical robots under the name Da Vinci .The purpose of this research is to examine the perception of RAS among healthcare workers in the UAE. RAS is an emerging and a growing field and is expected to have a positive impact on the surgical practices across the world and in the UAE. However, there is a lack of studies on the perception of RAS in the UAE. The next section illustrates literature that examined the use and perception of robotic surgery, followed by methodology, results, discussion, and conclusion.RAS has gained popularity in fields requiring precision, stability, depth perception and attention to intricate details that can often prove a surgery quite complex –8. From When taking into consideration such radically new technology, it is important to take in the perception of such use by the healthcare professionals. What are the benefits to the healthcare system, and do they outweigh the challenges and disadvantages they may pose? Is it cost-effective? What training is required for proper use of the robotic systems?The introduction of RAS in surgical practice has come with both numerous advantages and shortcomings. RAS offers many benefits including reducing blood loss, reducing surgical complications, revisions, and post-operative recovery time, better field of vision, removal of hand tremor , 13, 14.As with any new technology for such intricate procedures, one would have to take into consideration the training hours required for physicians to be able to use robotic surgical systems for maximal efficiency . This prTo date public and healthcare experts’ perceptions about robotic surgery in healthcare are largely unknown, yet some minor studies have investigated the topic. The public perception of robotic surgery was studied by Boys et al. The findings showed that 86% of the respondents were aware of RAS and thought of it as laser surgery or a scalrless surgery . The perCurrently, there are seven robotic systems in the UAE mainly in Dubai, Abu Dhabi, and Sharjah. Most information on robotic surgical systems pertaining to the UAE were found on healthcare institutional websites, owing to very few publications on RAS in the Middle East. In the UAE, several hospitals have implemented robotic surgery programs, such as Cleveland Clinic Abu Dhabi, American Hospital Dubai, and Al Zahra Hospital Dubai The American Hospital in Dubai was the first healthcare institution to establish a 4th generation da Vinci robotic system and are on their way to implement the ROSA Knee System in orthopedic surgery to provide even higher quality patient care . AdditioIt is in every physician’s best intention to provide the utmost care as well as latest medical and surgical practices for their patients. Considering patient factors that would result in a preference for a surgery performed by the hands of a skilled surgeon alone as opposed to with robotic assistance remains an important deliberation for the future establishment of RAS , 28. In Ultimately, RAS has a great potential to upgrade medicine and improve surgeries for both healthcare professionals and patients. However, its application to this day is more widely used in certain surgical specialties and procedures more than others due to lack of evidence pointing towards significant improvement in surgical outcome despite its many benefits . AdditioThis study is qualitative research that aims at exploring healthcare experts’ perceptions regarding RAS implementation and barriers that impede its adoption in the UAE. Perceptions and experiences can provide information on various barriers, which are not always portrayed clearly using quantitative methods but can only be retrieved from an in-depth understanding of processes and context . A documThe data has been divided into quantitative and qualitative sections. The quantitative data is provided below in Table The participant roles cover clinical and non-clinical duties in healthcare. For instance, 11 of our respondents were clinicians such as orthopaedic surgeons, urologists, laparoscopic surgeons, bariatric surgeons, and pharmacists. Six of the of respondents were non-clinicians such as CEOs, healthcare manager, health informatic specialists, and a program manager.Regarding the perception of robotic surgery in general, 52.9% of the study respondents perceive robotic surgery as a very useful medical technology and are planning to use it. As shown in Table In terms of provision of robotic surgery, 29.4% of the survey respondents already offer robotic surgery to their patients, and 35.3% of the respondents are not planning to offer the service. The rest of the respondents maintained that there is a plan to offer the service in the future, in which 11.8% are planning to offer it in two years, 17.6% in three years, and 5.9% in 1 year.Concerning the reason for implementing robotic surgery or planning to implement it, 23.5% of the survey respondents did not have an answer due to the fact that they do not offer robotic surgery services, 47.1% of respondents indicated that their hospitals decided on offering the service, for better clinical quality outcomes. Hospital’s reputation was the answer of 23.5% of the respondents, and the last 5.9% of our respondents implemented robotic surgery services to increase hospital’s revenues.With respect to whether offering robotic surgery services can enhance the performance and reputation of the hospital, 94.1% of the participants indicated that RAS could indeed enhance the reputation and performances of the hospital.Figure In terms of the challenges that might prevent the implementation of robotic surgery, 58.8% of the study respondents highlighted financial challenges as shown in Fig. With respect to the fact that whether the workload will increase or decrease when RAS will be implemented, about 52.9% of the participants believe that there is no change in the medical staff workload, and 29.4% of the respondents think that the workload will be reduced. Only 17.6% of the study respondents believe that the workload will increase.When comparing traditional surgeries to robotic surgeries, 18% respondents mentioned that traditional surgeries are cost effective. Around 44% respondents to this survey indicated that they have more flexibility with traditional surgery.Concerning the impact of the COVID-19 pandemic on the hospital plan to provide robotic surgery, 47% of the respondents indicated that COVID-19 pandemic did not change their plans and 53% of respondents indicated that COVID-19 changed their plans with regard to implementing robotic surgeries.The following section analyses and interprets that data from the interviews. Moreover, it explains the significance and implications of the information.Most of the study respondents were surgeons and clinicians and thus are better able to provide more relevant perception of the utility of robotic surgeries in their specialties. Our findings are similar to previous studies on the perception of the use of RAS in Saudi Arabia  and CanaMost of the study participants have a positive perception of RAS. Other studies examined RAS perception in Europe, US, Canada, Kuwait, and Saudi Arabia and in general found a positive awareness of RAS and a positive perception of RAS in surgery , 13, 19.The study results indicated that a third of the study participants are already using RAS. This is similar to previous studies , 29, 37.Approximately, 47.1% of study respondents clarified that their hospitals decided on offering the service to improve clinical outcomes , 13, 18.With the advances in medical technology, more and more medical specialities are adopting RAS. Similar to the findings of other studies, our findings show that 52.8% of the study respondents indicated that RAS is being used in general surgery  urology Robotic surgery is an expensive technology if we compare it with traditional medical methods. For instance, the cost of a spine surgery RAS system can range from $500,000 to $1 million US Dollars. There are also annual operational and maintenance costs . It is eAs indicated earlier, many physicians perceive RAS to improve the patient outcome and improve precision . Most ofWhen it comes to the differences between RAS and traditional surgeries, similar to other studies, the study respondents reported flexibly as the main advantage of traditional surgeries over robotic surgeries , 13, 18.COVID-19 pandemic did impact hospital plans to implement robotic surgery since most of the hospitals were focusing on safety and how to go back to normal operations . In addiThe purpose of this paper is to study the perception of healthcare experts on the use of RAS in UAE. Medical technology is advancing at a significant speed. RAS is one of the available medical technologies which has a great potential improve the outcomes of care. With better precision, shorter surgical time, and smaller incisions, it is expected that patients will heal better and develop less surgical complications.Our findings show that most of the study participants have a positive view on RAS, indicating that it is correlated with improving medical outcome and reducing surgical complications. The application of RAS within medical specialities is still limited to urology, general surgeries, and brain surgeries surgical procedures. Improving hospital reputation was cited as a main reason to adopt RAS. Perceived challenges included the high cost of RAS services and the lack of technical training. RAS device manufacturers could develop better creative strategies to lower the costs of ownerships to healthcare providers. The study respondents perceive that the COVID-19 pandemic lowered the adoption of RAS in the UAE. As the UAE is recovering and removing the restrictions following the COID-19 pandemic, it is expected that more providers will use RAS.One of the limitations of this study is the relatively limited sample size. A larger sample size may reveal variations in the perception and adoption of RAS among clinicians.More research should measure public opinions of RAS in the UAE. In addition, more research in the UAE is needed for measuring the outcomes of RAS and potential side effects.


Resilient healthcare research studies how healthcare systems and stakeholders adapt and cope with challenges and changes to enable high quality care. By examining how performance emerges in everyday work in different healthcare settings, the research seeks to receive knowledge of the enablers for adaptive capacity. Hospitals are defined as complex organizations with a large number of actors collaborating on increasingly complexity tasks. Consequently, most of today’s work in hospitals is team based. The study aims to explore and describe what kind of team factors enable adaptive capacity in hospital teams.The article reports from a multiple embedded case study in two Norwegian hospitals. A case was defined as one hospital containing four different types of teams in a hospital setting. Data collection used triangulation of observation (115 h) and interviews (30), followed by a combined deductive and inductive analysis of the material.The study identified four main themes of team related factors for enabling adaptive capacity; (1) technology and tools, (2) roles, procedures, and organization of work, (3) competence, experience, knowledge, and learning, (4) team culture and relations.Investigating adaptive capacity in four different types of teams allowed for consideration of a range of team types within healthcare and how the team factors vary within and across these teams. All of the four identified team factors are of importance in enabling adaptive capacity, the various attributes of the respective team types prompt differences in the significance of the different factors and indicates that different types of teams could need diverse types of training, structural and relational emphasis in team composition, leadership, and non-technical skills in order to optimize everyday functionality and adaptive capacity. Resilience in healthcare (RiH) can be defined as ‘the capacity to adapt to challenges and changes at different system levels, to maintain high-quality care’ p. 6 . The resHospitals are defined as complex organizations with high task demands and a large number of actors collaborating across time and space to deliver safe healthcare. The tasks and number of interactions are high and variability in performance frequently occurs . This inA common conceptualization of teamwork is “two or more individuals with specified roles interacting adaptively, interdependently, and dynamically toward a common or valued goal” p. 559 . TeamworThe aim of this study was to increase our understanding of adaptive capacity in four different types of hospital teams by exploring the temporal and dynamic features of teamwork and the contextual influences within which they operate. More specifically this study investigates how team factors  relate to teams’ adaptive capacity in four different team types in two hospitals. The following research question guided the study: what kind of team factors enable and hinder adaptive capacity in teams, and how do these factors affect adaptive capacity?A qualitative case study methodology was used to explore team factors and how they enable adaptive capacity in hospital teams. Qualitative research describes, interpret, and generate theories about social interactions and individual experiences as they occur in natural, rather than experimental, situations . The stuIn line with the study protocol  the two To gain initial access to the recruited hospitals we contacted their respective research departments, and researcher BF used her professional network to contact key personnel in the departments, enabling us to perform data collection during the Covid-19 pandemic. After receiving permission and access to carry out the study in both hospitals, we collaborated with the hospitals to identify and locate the four different team types in each of the hospitals; structural, hybrid, responsive and coordinating see . The leaWe collected data through observation, interviews, and document analysis. The data were collected between December 2020 and June 2021. Researchers BF and HBL conducted the observations of all the teams using an observation guide which was subsequently used to structure the writing of the field notes. Both researchers wrote their own field notes for each of the teams. The guide was developed in line with central concepts from the resilience literature, and essential features of hospital teams. This prompted the researchers to capture key aspects of work as done . The resWe used a semi structured interview guide based on content from the Concepts for Applying Resilience Engineering (CARE) model, i.e., demand, capacity, misalignments, and adaptations . And furObservation notes and interviews for each team were transcribed grouped together to simplify the analysis work. The analysis was performed with a combined deductive and inductive approach . We usedThe results of how team factors influenced adaptive capacity in the eight teams at the two hospitals are presented team-wise and structured according to the following four main themes: (1) Technology and tools, (2) Roles, procedures, and organization of work, (3) Competence, experience, and learning, and (4) Team culture and relations. See The structural teams were ward-based teams that consisted of nurses and nursing’ assistants working together on a permanent basis. They worked together in small units of 3–4 persons on each shift. Typical misalignments for the teams were the unpredictable workday, lack of staff, competence  and challenging peak situations (sudden high flow of incoming patients).An important enabling factor for the structural teams was the availability of technology and tools , and how the organization supported the teams by making these tools available, alongside the physical workspaces that corresponded with their needs. The structural team members in both hospitals carried a printed list containing names, diagnoses, and treatment plan for all patients in their pockets. The list was updated in the software and printed out at the start of each shift. The list not only helped the team members to easily assist each other in the treatment of patients, but also to monitor the overall status of the ward. In hospital 2 everyone also had their own mobile device. With this device they signed on to the care of their patients in a program that enabled other partners at the hospital to promptly contact them about their patients.Organization of work, the structure of the different shifts, clear role descriptions and procedures for work tasks were factors that provided the teams confidence to undertake their daily work. Although the teams had a plan, they were always prepared for it to change. Adaptive capacity was enabled by planning how to support each other, and by being mentally ready for changes to happen as part of a normal workday. Moreover, they prepared for the absence of team members who held additional roles outside the team , for example by avoiding allocating responsibility for the most severely ill patients to them.The results show how the organization of work took normal peak hours of the day into account by providing extra floating staff for these periods. The extra resources served the teams on the ward and completed requested tasks to reduce the misalignments of demand and capacities.A key factor for supporting adaptive capacity related to how the team members continuously updated each other within the team during the shift. Updates enabled the organization of relevant assistance for patients and preparations for emergencies, for instance in the case of a deteriorating patient. It was also important for the team members to know who was available to help out, and scheduled team huddles helped bring the team members up to speed.Competence was vital for the structural teams’ ability to adapt. Several daily work tasks required high professional competence, such as monitoring acutely ill patients, or patients newly transferred from the intensive care unit (ICU). Replacing competent team members with less competent members led to a redistribution of tasks, and increased responsibility for others with more competence. The team members’ experience was also important for adaptive capacity. When faced with challenges, experienced team members brought a sense of safety to the teams, and they supported new team members by providing advice. For example, knowing what to prioritize in peak situations was something new nurses found challenging to do as it has to be learned through working with more experienced colleagues. Competence development such as tutoring, training and simulation was offered to structural teams, but often competed with the daily chores.The structural team members supported each other in carrying out their work. A culture of helping each other was fundamental for their adaptive capacity, for example by making sure that everyone got a break during the shift or helping with tasks if one of the members was struggling. Findings show that team members who work regularly together on weekend shifts became very familiar with each other and thus developed their own structures for the division of responsibility and support for each other. Team members talked about knowing each other personally and professionally. The division of responsibility on a shift was easier if they knew each other’s preferences, strengths, and weaknesses. It was also easier to ask for help, or admit that they did not know, or were uncertain about something. They talked about how team members reluctant to help others, were counterproductive for the team’s adaptive capacity.The hybrid teams had a permanent staff of nurses and a rotating staff of physicians. The two teams observed in this study were situated on short stay units receiving patients with a wide range of diagnoses who were admitted 24 h a day, 7 days a week. Typical misalignments for these teams were lack of staff, peak situations with high inflow of patients, erroneous triaging of incoming patients, and rapid workflow changes where the team members quickly needed to respond.The hybrid teams in both hospitals worked in well-equipped wards with premises well-suited for their work, with open spaces and short distances enhancing the prospect of monitoring and assisting colleagues when needed. In hospital 2 they used a software program that indicates incoming patients and their status to monitor, plan and prepare for admissions.Receiving acute admission patients with a range of diagnoses, meant that the team needed clear procedures and role descriptions to structure their work. Patients were distributed within the team according to capacity, and the team members were prepared for unpredictable workdays. As in the structural teams, the hybrid teams were supported with extra floating staff to reduce workload and avoid high pressure situations during known peak hours of the day. In hospital 1 both nurses and physicians were physically located together, at the same work desk, and thus worked closely to improve information flow and communication.In hospital 2, all patients were triaged into red, yellow, and green by ambulance personnel prior to admission. However, the coding of patients was often found to be misleading, leading to a need for the team members to reallocate resources and take on extra work to ensure quality in care in such situations.Some hybrid team members held additional roles outside their team. Stroke alarm and cardiac arrest alarms were assigned to both nurses and physicians. In hospital 2 certain tasks and responsibilities came with the requirement of a certain level of competence, with three specified competence levels for the nursing group , and two for the physician group .The shift leader maintained an overview of the ward, and planned the allocation of incoming patients, and the transfer of patients to bed wards. By having an overview of the total situation, the shift leader could handle high pressure situations by reallocating resources to where they were needed. This supported the overall adaptive capacity of the team.The distribution of roles and responsibilities among team members reflected the competence levels of both nurses and physicians. This was of high importance especially in hospital 2, due to the many roles and competence requirements the team had to cover on a shift . Both hybrid teams received patients with a large range of symptoms and the teams’ task was to decide on a diagnosis and quickly start treatment. The range of possible treatments made it difficult for all team members to be familiar with all of them, so to a large degree they relied on written procedures. The less experienced the team members were, the more they relied on procedures. Several of the procedures and the different equipment in use in both hospitals required competence to operate, as well as recertification on a regular basis. This led the team to regularly undergo retraining. Both teams also conducted simulation training on a frequent basis to learn and maintain skills in undertaking critical procedures, and to handle and adapt to situations under stress.Both the hybrid teams were characterized by a supportive culture, where they tried having lunchbreaks together, or offered help to colleagues. In particular, the leaders in hospital 1 had put major effort into reducing the hierarchy between nurses and physicians in the team. The leaders insisted that the team members should be situated next to each other during their workday to enable information transfer and collaborative decision making. However, for the rotating physicians, familiarizing themselves with this structure was a challenge. Similar to the structural teams, the hybrid team members talked warmly of each other and found weekend shifts to be especially useful for building relationships with colleagues, as members worked closely together and developed their own routines and culture during these shifts. To enable adaptive capacity, the team members depended on helping each other to even out the workload by taking on extra work and changing the responsibility for tasks when needed.The responsive teams in our dataset were teams who responded to suspected incidents of cerebral infarction. The teams were multi professional, consisting of members from different professions and departments. The responsive teams only met and worked together during short, unplanned, and acute episodes. Typical misalignments for the teams were workflow deviations , personnel  and patient needs . The teams regularly experienced that the patients were not in fact suffering a stroke, and that while they were focused on a rapid diagnosis of stroke other factors might be missed. This could lead to disagreements within the team, where some team members wanted to proceed with the stroke procedure, whilst the physician as the team leader wanted further investigations to avoid missing other important conditions.The responsive teams relied heavily on technical aids that enabled them to perform promptly. All members carried a calling device, and they could monitor incoming patients using the hospital computer system. The physicians especially aimed to make use of this tool by having one eye on the screen whilst attending to other patients. This enabled preparation by reading the patient’s journal notes before the patient arrived. In both hospitals the computed tomography scan (CT) machine was located next to the emergency room (ER) for quick access, though in hospital 2 they had to use a more precise CT machine on another floor when the patient’s symptoms were uncertain, leading to a delay of diagnosis. However, the team adapted by performing some of the examinations during transportation to reduce the delay. Furthermore, the team had a medication bag including all necessary medication and equipment. To enable speedy work processes, the bag also included predefined medication dosages for quick administration of drugs and to eliminate the risk of miscalculations.The responsive teams had a very clear division of roles and organization of work. Everyone had their specific role with assigned tasks and followed a set procedure in order for the diagnostic process to proceed quickly. The stroke procedure was designed for everything to happen rapidly in order to start treatment as soon as possible for a better patient outcome. The clear procedure also enabled them to adapt when the patient’s condition changed or there was disruption of personnel.The responsive teams had to make rapid and often life critical decisions to diagnose and activate stroke treatment as appropriate. These teams thus depended on the highly competent team members making the correct decisions and performing the stroke procedure quickly. To enable adaptive capacity, the team used simulation-based training for developing individual skills, ensuring a clear role understanding, and rehearsing the stroke procedure. However, since the team members did not know each other well, they emphasized the importance of training non-technical skills like communication and team management in the simulation sessions. These skills were seen as crucial for adaptive capacity in the responsive teams. During the simulation training the facilitator offered time for reflection. The lack of regular spaces to talk about their work, implied missing potential important learning points. To compensate some of the members talked about incidents with their leader or colleague when returning to their workplace.Since the responsive teams did not work together on a regular basis, there were limited opportunities to develop strong relationships between the team members. In hospital 1, the team had a different composition every day, and only a few of the members knew each other. In hospital 2, the team members generally knew each other from previous stroke episodes. However, the episodes of teamwork were short and characterized by an acute atmosphere, leaving little time to get well acquainted. Furthermore, they were reliant on a team culture of mutual understanding of their roles and tasks. Knowing each other enabled adaptations from set procedures. Experienced team members were more confident in doing adaptations.The coordinating teams met to allocate patients to available beds to avoid bottlenecks in peak periods and even out the total demand and capacity of the two respective hospitals. The teams consisted of all bed ward managers in each hospital. They had daily 10-min meetings during the day shift to allocate patients and resources. Like in the responsive team, the coordinating team members did not work together on a regular basis, and only met for short episodes. The major misalignments of the teams were inconsistent patient numbers and a complex hospital organization where decisions about evening out demand and capacity were made across time and place. For example, the ward managers could agree on moving patients, but they needed approval from the chief physician who could disagree, leaving them to work out another solution. Also, a mismatch in conceptualizing bed capacity across wards caused challenges for team decision making. In particular, it was difficult for the members to accept/agree that some wards needed to have free buffer beds reserved for acute patient demand, for instance for Covid-19 patients, whilst other wards had to take on extra patients and overbook their capacity. This often led to discussions and disagreements within the coordinating teams. The different cultures the members represented also became visible with some team members often offering to help, while others were reluctant. The team leaders said that this was often the same members who were either positive or negative. There were few opportunities for reflecting on and learning from previous experience, and little sharing of learning.To get an overview of the total bed capacity status at the hospital the teams needed information decision aids and tools. As mentioned, in hospital 1 the coordinating team members took advantage of software to report bed status prior to the meeting. A lot of effort and work had been done to update the computer software system to deliver the correct information about current bed status. Hospital 2 used an online tool that showed the current bed status of every ward. However, due to the rapidly changing nature of the wards’ bed status, both the coordinating teams needed additional information from the team members to make informed decisions on the reallocation of patients and beds across the hospitals.Procedures and organization of work were important in both coordinating teams. The team in hospital 1 needed more structure because it was a larger team than in the smaller hospital 2. In hospital 1 the team members each reported the status of the current capacity on their ward via a webpage prior to the meeting. The coordination meeting was led by a coordinator from the emergency department (ED) and followed a specific agenda. In hospital 2 the meeting had no firm agenda, although the objective of the meeting was similar to that in hospital 1. In hospital 2 the team had a procedure which they roughly followed, but they usually agreed on the required actions to close the bed capacity gap. The team in hospital 2 consisted of significantly fewer members than in hospital 1, and they all knew each other well, although replacements occurred. In hospital 2, in situations when the overall bed capacity was ok, and no actions were needed, the team members used this meeting to raise other challenges and supported each other in difficult decisions.When the overall bed capacity situation was very uncertain, the teams in both hospitals could either agree to wait and see whether the situation would resolve itself without intervention or arrange for a second meeting later in the day to reassess the situation.The coordinating teams used competence and experience in their decision making. The team members often used their prior experience when reallocating patients or negotiating for beds among themselves, knowing what to prepare for and expect in a situation. Based on their experience they could often foresee how a situation might develop, and as such, the appropriate alternatives for solving challenges. The more experienced leaders enacted a more independent voice in the team meetings and were listened to more by others.Team culture and relations differed in the two observed coordinating teams. In both teams, the team culture affected their capacity to adapt to challenges and changes. Hospital 1 had a large coordinating team with a formal atmosphere and limited to no time for informal conversations. Most of the members were not well acquainted and the meeting had a very strict agenda leaving little time to get to know each other. The leader systematically worked to build a culture where the team members would feel responsibility across wards and create a supportive culture and relations between wards to better adapt and improve the overall situation of the hospital’s bed capacity. In hospital 2, team members knew each other well, and used each other for problem solving, and to give or receive advice. When the overall capacity situation of the hospitals was good, the teams functioned well.In this study we have explored the role of team factors for adaptive capacity in four different types of hospital teams  in two hospitals. We found that the main team related factors of importance for enabling adaptive capacity were (1) technology and tools available to the teams; (2) clarity and description of roles, procedures, and the teams’ organization of work; (3) the teams’ competence, experience, and knowledge which also enabled sound learning processes; and (4) the team culture and relations among the members. In the following we discuss our results in relation to previous research and suggest future research and implications for practice.Our results are in line with previous research on teamwork and advances the literature on teams . Our stuAs For the responsive and coordinating teams, however, it was challenging to develop these relations due to not working closely together on a regular basis. Instead, they compensated for this by having structural factors in place, like clear role descriptions and procedures that allowed them to function well as a team. Competent and experienced team members added to the likelihood of their success. In addition, the responsive teams regularly undertook simulation-based training focusing on communication to compensate for the lack of close relationships between the team members, as these teams only come together to perform specialized tasks for short periods of time.In addition, the coordinating team in hospital 1 was heavily reliant on their software program to understand the total bed capacity situation, mainly due to the size of the team. The larger team in hospital 1 faced greater coordination challenges and had more difficulties developing and maintaining relations than smaller team in hospital 2 . The cooPrevious research argues that aligning flexibility with stability is key for enabling adaptive capacity in teams . RelatinOverall, this study demonstrates that the adaptive capacity of all team types depended on the four main factors identified. However, the varying influence of the factors within the different teams, as depicted in in situ observations of the teams, teamwork is dynamic and team members react to each other’s words and behaviors’ and to the demands of the environment (The study has some strengths and limitations. It is a major strength to combine observations and interviews in a total of eight teams in two different hospitals. This provided us with a rich material to understand adaptive capacity in teams and how team factor enables this. Although we have performed ironment . AdaptatThis study has shown that factors for adaptive capacity in hospital teams must be seen in relation to the distinct attributes and circumstances of the teams in question. The key team factors that enable adaptive capacity are related to the technology and tools available for the teams; the specification of roles, procedures, and organization of work within the teams; the team members’ competence, and experience, and the internal culture in the team and the relationships between team members. We found that the influence of these factors on team adaptive capacity varied according to the team types. Adaptive capacity in structural and hybrid teams was mainly dependent on relational and cultural factors and by the team being co-located, as these teams had stable membership and roles. The responsive and coordinating teams, however, needed clear structures, roles, and tools and technology available to support their adaptive capacity, as the relational dimension was not as influential on task execution and adaptations within these teams as compared to structural and hybrid teams.Our results imply that the systems supporting hospital teams must consider the teams’ strengths and weaknesses when organizing in and around the teams. Resilient responses to changes and challenges requires resources that are both relational and organizational . The adaThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Regional committee for Medical and Health Research Ethics. The patients/participants provided their written informed consent to participate in this study.BF and HL conducted the data collection. BF conducted and transcribed all the interviews. BF, HL, VG, JA, and SW conducted the analysis and interpretation of data. All Authors developed the study design, contributed with writing, critical revision, and approval of the final version.This PHD project is part of the Resilience in Healthcare Research program which has received funding from the Research Council of Norway from the FRIPRO Toppforsk program, grant agreement no. 275367. The University of Stavanger, Norway, NTNU Gjøvik, Norway supports the study with kind funding.SW was member of Frontiers editorial board special topic; Occupational Health and Organizational Culture within a Healthcare Setting: Challenges, Complexities, and Dynamics.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The radio electric asymmetric conveyer (REAC) is a technology that has the purpose of restoring the cellular polarity triggering the rebalancing of the endogenous bioelectric field, which considering the neurological dysfunctions, affects the neural communication mechanisms. The studies published so far show that the REAC neuromodulation technology has positive effects in treating these dysfunctions, with the principles of endogenous bioelectricity as a basis to achieve these effects.This study aims to review the literature that explored the effects of REAC protocols on motor control and to identify which mechanisms would be involved.This integrative review considered studies that used REAC as a therapeutic intervention directed at human motor control and experimental research with animals that applied REAC to obtain effects related to motor behavior.Ten articles were included, eight clinical and two experimental studies. The clinical studies used the neuro postural optimization (NPO) protocol in 473 patients, of which 53 were healthy subjects, 91 were Alzheimer's disease patients, 128 were patients with atypical swallowing, 12 subjects with neurological diseases, and 189 were without the specification of disease. The experimental studies used the antalgic neuromodulation and neurodegeneration protocols in animal models.The information integrated in this review made it possible to consider REAC technology a promising resource for treating motor control dysfunctions. It is possible to infer that the technology promotes functional optimization of neuronal circuits that may be related to more efficient strategies to perform motor tasks. The radio electric asymmetric conveyer (REAC) technology aims to elicit cellular reprogramming activity and modulate adaptive responses in the body by restoring the balance of cellular endogenous bioelectric fields . It has The REAC technology works through the interaction of the radioelectric field produced by the REAC device with the endogenous bioelectric fields produced by the cells. After establishing this interaction, a series of cellular ionic fluxes are generated, triggering transcriptional and signaling events that drive reprogramming and differentiation decisions of cells affected by this interaction , 15, 16.Scientific research published to date has shown that REAC neuromodulation technology has positive effects on the treatment of central neurological disorders such as depressive syndromes , 18, anxIn addition, the effects of REAC protocols seem to be beneficial for slowing down the processes of neurodegenerative diseases  and the Although some studies , 37–39 hThe present study is an integrative review . We haveTo be included in this review, the studies should have applied REAC protocols and observed outcomes related to motor control. Studies that did not provide information on the REAC protocol or describe how it was applied would be excluded.The screening process began with the title analysis to verify the use of REAC protocols. After the initial screening, the selected studies were retained for analytical reading of the abstract and verification of eligibility criteria. Those potentially eligible were analyzed in full detail, and those that did not fit the eligibility criteria were excluded.The information extracted from the included studies were: title, author, year, country of origin, and type of study. Descriptive data from clinical studies were: sample , assessed motor task, outcomes, and assessment methods. In experimental studies, the animal model, the model's characteristics, the motor task, and the outcome evaluation methods were considered. Additionally, information about REAC protocols was extracted from both clinical and experimental studies.Two reviewers carried out the entire methodological process of this review and the inconsistencies would be resolved by a third reviewer.Through the search engine, 283 studies were found, of which 234 were excluded by duplication and title because they did not use the REAC technology. Of the remaining 49 studies, 39 were excluded because they did not present motor control as a primary or secondary outcome. As a result, ten studies were reviewed. The process of identification, screening, and inclusion of studies is summarized in Out of the ten studies included in this review, eight were clinical studies and two were experimental, with animal models. Three of the clinical studies had a prospective longitudinal design, three were randomized controlled trials and one was retrospective.The reviewed articles were published in the last 10 years, most of them between 2011 and 2015. Most of the studies were developed in Italy, specifically at the Instituto Rinaldi Fontani located in the city of Florence. Two studies were carried out in Spain at the Alzheimer's Center of the Reiná Sofía Foundation.The characteristics of the studies regarding the type and country are given in The clinical studies evaluated 473 patients. The age of the research subjects was quite heterogeneous among the studies and the averages ranged from 9 to 86 years. There was also great variability in terms of clinical, functional, cognitive, and psychological characteristics. Altogether, the studies evaluated 53 healthy subjects, 91 with Alzheimer's disease, 128 patients with atypical swallowing, 12 subjects with neurological disorders  and 189 without specification of the disease.Details of the clinical studies with humans are presented in Regarding the REAC protocols, all the clinical studies used the neuro postural optimization (NPO) protocol. Castagna et al. used the neuro psycho physical optimization (NPPO) protocol, in addition to NPO . The expvia the specific probe called asymmetric conveyer probe (ACP). In the REAC NPO treatment a specific ACP is positioned on a precise area of the auricular scapha. In the NPO protocol the specific absorption rate of 7 µW/kg for a current density of 7 µA/cm2 and a distance of 150 cm from the emitter. With these parameters, a radio electric field of approximately 20 µW/m2 is generated , according to Rinaldi et al., , consistThe NPPO treatment aims to reorganize the overall dysfunctional adaptive changes, also on an epigenetic basis, which are stratified during life. Furthermore, NPPO treatment is intended to prevent them. To try to achieve and maintain this goal it is necessary to repeat the treatment cycles as environmental interaction and allostatic pressure are unavoidable. The generally recommended interval between cycles of the NPPO protocol is approximately six months.The study by Zippo et al., 2015, used the antalgic neuromodulation protocol (ANM) in animal models, with pulses repeated every 1.5 s . The aimThe purpose of this integrative review was to seek evidence to explain the mechanisms of action of REAC technology to modulate neurophysiological aspects involved in motor control. In addition, it was also aimed to identify in the studies the protocols, parameters, and forms of application of the REAC used to act in the mechanisms of motor control. For this, we sought clinical and experimental research in the databases that used REAC and investigated motor outcomes. As a result, it was possible to select 10 studies, 8 clinical and 2 experimental.According to Frohlich  synchronUntil recently, these endogenous electric fields were considered an epiphenomenon of brain activity, but it is currently understood that this energy is able to change protein metabolism, the process of cell differentiation, electrical and chemical synaptic signaling, the process of inflammation, tissue repair and pain perception . Some stSo, is the presence of endogenous electric fields just an epiphenomenon? Or can it be considered a necessary event for the amplification of neuronal communication?.According to Fries et al. , the neuTherefore, endogenous bioelectric fields seem to be fundamental to aiding synchronization in neuron communication, especially when it comes to motor control. Motor control, therefore, depends on the coordinated and stable cortical activity that is partly due to the electric field generated by the simultaneous activity of the cells that make up the brain tissue, both neurons and cells of the glial system .Therefore, the normality of the bioelectric field in the central nervous system depends on the morphological and functional integrity of the cells and, thus, neurological and psychiatric diseases are related to changes in bioelectricity, which contribute to stereotyped responses of brain functions , 51, 52.Three studies included in this review –14 showeThus, reduced brain activation in the motor cortex detected by fMRI may be related to a more efficient motor strategy with fewer brain areas being activated, supposedly only those which are strictly necessary. In fMRI, brain  activation during motor activity is represented by increased or decreased BOLD signals . This siTherefore, reductions in the BOLD signal can represent a decrease in brain activity determined by the reduction of local blood flow or an increase in the neuronal metabolic rate that determines the presence of deoxygenated hemoglobin , 55. WhaIt is important to highlight that reducing brain activation means, in this case, a “functional optimization” of the neuronal circuits as, to perform the same task, the subject uses fewer neural resources, increasing the system's efficiency.One of the studies included in this review  sought tAccording to Berge, the neuronal processing of pain hinders the segregation and integration of information in the thalamic cortical circuits due to the sensory overload that determines changes in the electrical fields around the neurons responsible for this processing . Pain prThe studies of Rinaldi et al. (2011) and 2014) found similar results in brain activation, especially the disappearance of the activation signal of the thalamic component after the NPO protocol of REAC , 14. It  found siin vivo studies (The reorganization of endogenous electric fields seems to be responsible for these effects on the nervous system, as it is known that these fields are fundamental for synaptic communication, especially when it comes to motor control . Lorenzi studies  indicate studies .Thus, it can be assumed that the application of REAC technology protocols can optimize neuronal communication by favoring the containment or removal of amyloid plaques. Thus, the establishment of a balanced electric field possibly influences the activity of neurons inducing the activation of cells that would be responsible for transmitting information , 13. ThiThese explanations may be related, for example, to the results of one of the studies included in this review: Castagna et al. aimed to test the effectiveness of REAC brain stimulation in atypical swallowing . AtypicaAccording to Hebling et al., stress and anxiety seem to be related to swallowing abnormalities, especially in the oropharyngeal phase of the process. The oropharyngeal phase consists of the sequence of events that begins with the voluntary contraction of the muscles of mastication and gradually passes into involuntary control. This process is mediated by mechanisms that involve integrating information from the swallowing center in the brainstem with the motor cortex through the basal ganglia . TherefoAccording to the developers of the REAC treatment, Rinaldi and Fontani, environmental stress is responsible for causing morpho-functional changes in the central nervous system capable of inducing the development of functional dysmetria . This moClinical studies that evaluated the effect of REAC on motor tasks showed that the technology is able to improve motor response through functional optimization of neural circuits. The experimental studies included in this review indicated that REAC technology influences glial cell activity and improves neuronal synchronization by reducing local field strengths. These findings may be a promising basis for elucidating the mechanisms involved in the modulation of technology-induced neuronal bioelectrical activity.


Solid rocket motors (SRMs) have been popularly used in the current aerospace industry. Performance indicators, such as pressure and thrust, are of great importance for rocket monitoring and design. However, the measurement of such signals requires high economic and time costs. In many practical situations, the thrust measurement error is large and requires manual correction. In order to address this challenging problem, a lightweight RepVGG-based cross-modality data prediction method is proposed for SRMs. An end-to-end data prediction framework is established by transforming data across different modalities. A novel RepVGG deep neural network architecture is built, which is able to automatically learn features from raw data and predict new time-series data of different modalities. The effectiveness of the proposed method is extensively validated with the field SRM data. The accurate prediction of the thrust data can be achieved by exploring the pressure data. After calculation, the percentage error between the predicted data and the actual data is less than 5%. The proposed method offers a promising tool for cross-modality data prediction in real aerospace industries for SRMs. Solid rocket motors have been popularly used in the current aerospace industry. With their high specific impulse, simple structure, and safety features, SRMs have become a widely used propulsion system in missiles and other military weapons. The basic components of an SRM include combustion chambers, nozzles, and igniters, with the propellant’s thermal energy converted into kinetic energy to generate thrust ,2. AchieDuring the testing of solid rocket motors, the traditional method for measuring thrust involves installing the motor on a reaction platform, initiating the motor for testing, and recording the force exerted on the platform . AccordiData-driven AI technology has gradually emerged, and deep learning is one of the current representative technologies of AI. In terms of data measurement, some people have proposed algorithms for data measurement and achieved good prediction results, including artificial neural network (NN) , convoluIn recent years, methods for using deep learning to solve SRM problems have received more and more attention. Liu et al.  proposedWilliams et al.  proposedDeep learning has many applications in mechanical fault diagnosis. Peng et al.  proposedResearchers are increasingly applying deep learning methods to the performance parameters of SRM. The performance parameters of SRM, such as internal ballistic parameters and specific impulses, are essential indicators to measure the performance of SRM. Deep learning has great potential for the calibration of SRM parameters.Deep learning provides critical information that traditional measurement methods are unable to obtain, reducing the cost of measuring motor parameters. However, the research on the SRM thrust measurement method using deep learning is lacking. Traditional methods for measuring thrust data are often inefficient and have low measurement accuracy, and SRM experiments require a significant amount of data to support them. To address this issue, this study proposes the use of deep learning to predict thrust parameter data for SRMs. We validate the feasibility of this approach by comparing the generated data with actual experimental data obtained from motors. The results demonstrate the effectiveness of this method in generating accurate thrust parameter data, which can serve as a valuable supplement to the ground experiment process of SRMs.The primary objective of this study is to collect pressure and thrust data from ground experiments of SRMs using sensors and preprocess the obtained data into training and test sets. Subsequently, the collected data will be utilized to develop a predictive model for thrust data, utilizing deep learning techniques. The data processing phase will comprise several essential steps, including filtering, normalization, and feature extraction. The deep learning models will be trained using a training set, and their performance will be evaluated using a test set. The optimal parameters of the model will be determined using experimental comparison methods, and the model’s performance will be assessed using metrics such as mean squared error and mean absolute error. The process is illustrated in This section primarily focuses on the system composition and thrust data prediction process of the lightweight one-dimensional RepVGG network. The training process predominantly employs the convolution module within the model to conduct convolution operations on the motor parameter data, thereby capturing data features. The network model’s composition primarily comprises one-dimensional convolution, RepVGG block , and droThe method is to add the RepVGG blocks to the convolutional neural network according to the motor data pattern and make changes to the RepVGG blocks suitable for the parameter data of SRM. The network model is shown in These data are input into the network, and after being processed and added separately by the two branches of the first layer, the data are transferred to the stacked RepVGG block. The filter size in the RepVGG block is 3 × 1, allowing the filter to capture the data features. The filter stride is set to 2 to reduce redundant feature extraction. Dropout is added before the network’s output layer to select advanced features and mitigate overfitting. All convolutional layers utilize the ReLU (Rectified Linear Unit) activation function and are optimized using the Adam algorithm. In comparison to existing CNN network structures, the network architecture employed in this study demonstrates superior accuracy in predicting motor parameters. This particular network structure is well-suited for extracting deep features related to motor parameters and extracting valuable information from abstract data.Convolutional neural networks (CNNs) were initially proposed for image processing and have achieved impressive results in performing image classification tasks due to their characteristics, such as shared weights and local perception. CNNs have also shown remarkable success in natural language processing, speech recognition, and other fields. The convolutional layer convolves filters with input data to generate features, which are then extracted through pooling operations and learned by stacking filters. While two-dimensional (2D) convolution is commonly used in processing image data, this article uses one-dimensional (1D) convolution to build the model because of the nature of the data. The input sequence data entered are The generated new sequence y extracts feature through convolution operation and downsampling processing, and through the superposition of the convolutional layer and sampling layer, the network continuously extracts and learns important features and finally obtains the model.In general, the greater the number of filters and layers of convolutional layers and the higher the accuracy of prediction, the greater the computational burden, so it is necessary to weigh it in conjunction with actual research.The RepVGG network has a high inference speed and is suitable for predicting the thrust of SRM. In this study, the lightweight configuration of the RepVGG network was carried out. The attention mechanism was added following the backbone structure of the RepVGG network, further improving the network’s performance.RepVGG is a network modified from the VGG network . This stThis model employs a multi-branching structure, and parallel multi-branching generally enhances the model’s characterization ability. The overall model structure is simple, which separates model training from model inference. This separation facilitates accelerated inference and makes the model faster and more accurate. Additionally, the attention mechanism SENet  is incorIn the fusion process of convolution and BN , the formula of the convolutional layer is defined as follows:The BN layer formula is defined asSubstituting Equation (3) into Equation (4) is obtained.After simplification, the following equation is obtained, which is denoted asDropout is a method commonly employed in the training of deep learning models, where a portion of the neurons in a neural network are temporarily excluded during the training process. This exclusion is performed randomly, resulting in a subset of neurons being utilized for forward propagation and backpropagation. By selectively deactivating neurons in each training session, dropout effectively reduces the interdependence among them, thereby enhancing the network’s ability to generalize.The primary goal of dropout is to address the issue of overfitting, which arises when a model becomes too focused on the training data and fails to perform well on unseen test data. By preventing neurons from relying too heavily on specific features or patterns, dropout encourages the network to extract more diverse and robust representations from the available data.In our specific study, we encountered a limitation in terms of the dataset we had at hand. We collected data from ground trial tests of solid motors, but due to the high costs and time associated with conducting such tests, our dataset is relatively small. Consequently, there is a heightened risk of overfitting when working with this limited amount of data.To address this challenge and improve the resilience of our model, we decided to incorporate the dropout technique. By randomly deactivating a fraction of neurons during training, we aim to reduce the risk of overfitting and improve the generalization capability of our model. This approach is particularly valuable when dealing with small datasets, as it helps prevent the model from relying too heavily on specific patterns or features extracted from a limited sample.The network model prediction process is divided into three stages: data preparation; model training; and model testing. The flow chart is shown in The data used in this study were obtained from ground test experiments of SRM, where professional sensors were used to sample the combustion chamber pressure and motor thrust, and the collected data were calibrated. However, when collecting data from different models of solid motors with varying operating conditions, the data were collected for different lengths of time, with varying frequencies and numbers of data points. These differences were not suitable for deep learning model training. To facilitate subsequent model learning, the time lengths corresponding to the thrust sequence data and pressure sequence data of the solid motor in the effective operating section were normalized so that the time range was between 0 and 1.During solid motor testing, the thrust magnitude rises rapidly from zero during the ignition process . The thrTo test the predictive effect of this model, the mean squared error (MSE) and mean absolute error (MAE) were used as the evaluation functions of this model.We present 14 sets of the real data used for training and the corresponding predicted data by downscaling them into a single point in the graph, where each point represents a data line. The distance between data points reflects their relative distance in two dimensions, and two data lines that are very close together in two dimensions will also be close together in the graph. Conversely, if the two data lines are far apart in two dimension spaces, they will also be farther apart in the graph.As shown in The predicted result of the lightweight RepVGG method is shown in In this section, the predictive performance of the lightweight RepVGG method is demonstrated by experimental methods, and the superiority of the lightweight RepVGG method is verified by comparing it with other popular neural network structures. The accuracy of this method was demonstrated by comparing the predicted results with the SRM ground trial test data. The experimental method shows the influence of different training sample numbers, filter sizes, block numbers, and window sizes on the prediction performance of the lightweight RepVGG method.This study proposes deep learning architecture as an accurate and efficient prediction method. To show the superiority of this approach, this paper compares this network architecture with other well-known network architectures. These include the deep neural network (DNN) , long shThe contrasting deep neural network contains three hidden layers, and the number of neurons in the hidden layer is 50, 50, and 100, respectively. The data in this study were measured by SRM ground tests. Due to limited data, the profound network architecture is difficult to train effectively and easily leads to overfitting, so the number of hidden layers and neurons in the model cannot be large. To reduce overfitting, dropout is applied to the last hidden layer.LSTM is a better network than RNN. RNN can handle specific short-term dependencies, but when the sequence is long, the gradient at the back of the sequence is difficult to backpropagate, and there is a problem of gradient disappearance and explosion, and LSTM can solve this problem. This paper will use a more powerful LSTM instead of traditional RNN as a comparison for this experiment. After testing, two LSTM layers and one fully connected layer were finally used for this experiment, and dropout was also applied to the network to improve the overfitting phenomenon.ResNet is an excellent network for processing images. In this study, the network configuration of ResNet34 was tried as a comparison model, but it was found that the effect could have been better due to the profound number of network layers. The network can be improved by trying to find how the 12-layer ResNet network works best; dropout is also added to the network.One of the reasons why VGG network architecture was chosen as an experimental comparison in this study is because RepVGG is an improvement based on VGG, and comparing the two shows the superiority of RepVGG to a certain extent. Of course, for fairness, VGG and RepVGG are identical in parameter design and have the same number of network layers.In all the comparison methods in this study, the model’s input layer and output layer are the same as the lightweight RepVGG method, and the model parameter update uses backpropagation using the Adam optimization algorithm. Epochs are the same and use MSE as a loss function. All networks have also added dropout to improve network overfitting.The batch size explicitly influences the network’s performance, and the batch size of 14 samples has the best effect. In the method of variable learning rate employed, the first 500 epochs of the training process are set to 0.001 to optimize the network quickly and then use the learning rate of 0.00005 for accurate convergence. Finally, the test data are input into the trained network for evaluation, and the accuracy is evaluated against the real data.By integrating the results predicted by different methods, the results are shown in In this part, we study the effect of experimental parameters on the model test results. The size of the filter and the number of blocks also have an effect on the prediction performance of the model, and we experimentally documented model errors when we tested them with different filter sizes and different numbers of blocks. The lightweight RepVGG method investigates the impact of the convolutional filter size on network performance. An experimental method is employed to compare the effect of different filter sizes on test accuracy, and the model training time is also measured. In response to the problem of scarcity of thrust data and large measurement errors of solid rocket motors, this paper proposes a lightweight RepVGG-based method for cross-modality data prediction of SRM. By processing the raw data, a model suitable for solid rocket motor data is built with the aim of predicting thrust data by inputting the pressure data of solid rocket motors. By training the model sufficiently, this method achieves good results on the test set. The predicted results show small errors and are close to the actual data, particularly in the rising and smoothing sections.Compared with other popular methods, this method shows the accuracy and effectiveness of prediction. The influence of the number of training samples on the model test results is studied. It is found that the prediction effect is best when the number of training samples is 14. At the same time, the influence of the window size on the model test effect is also analyzed. Through experiments, it is found that when the window is set to 200, the prediction effect of the model is the best. Through experiments, it is found that the number of model blocks has an important impact on the test results. Correct selection of the number of blocks has an important impact on model testing. During data preprocessing, the original data are large, and the sampling frequency of the data is different. The first 200 data points are directly intercepted during processing to prepare samples.Although the method in this paper has achieved good prediction results, the predicted data are also close to the real rocket motor experiment data. However, the training data set is smaller due to the scarcity of rocket motor data. The model trained on small data sets is prone to overfitting. Although the overfitting can be alleviated by adding dropout, it will still affect the test of the model. In addition, the sample is prepared by directly intercepting the first 200 data points, which also has certain limitations. Therefore, future work can employ data interpolation methods to insert new data points between existing ones, thereby increasing the density and capturing more details in the data.