When organizational models influence the intention to leave of professionals. The case of Family and Community Nurses in the Tuscan healthcare system

When organizational models influence the intention to leave of professionals. 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The case of Family and Community Nurses in the Tuscan healthcare system chiara barchielli, milena vainieri, lorenzo taddeucci This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3815418/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To describe Nurses’ Intention to Leave (ITL) their job across Tuscany -Italy- after a major healthcare structural policy redesign of the community organizational care delivery model, namely the Family and Community Nurse (FCN) model. Methods In this organizational case study, we evaluate the differences in the effects on Nurses’ ITL through the comparison between non-FCNs and FCN’s answers to the region-wide administered Organizational Climate Survey (OCS) conducted in 2023, after the model’s implementation. Results A general decrease in all ITL dimensions was registered, indicating, therefore, a higher FCNs’ intention to stay. There are variations in the answers from Nurses working in different Local Health Authorities (LHAs), and it is due to the uniqueness of organizations and to the dissimilar levels of maturity of the organizational models within them. Conclusions The effects of structural policy redesign on Nurses’ ITL highlight the importance of tailored, region-specific approaches to improve Nurses’ retention in the healthcare sector, which is currently experimenting the worst Nursing shortage crisis ever. Intention to leave Nurses Organizational model healthcare restructuring policy/intervention Strengths and limitations of this study The real-world evidence reported are the first reliable assessment of the ITL among Family and Community Nurses and Nurses in Italy. Tuscany has been a field for a natural experiment, a major healthcare structural policy redesign. Although the sample size and the evidence retrieved, it is a regional study, hence results cannot be scalable. Background Periodically, health systems undertake specific policies and structural redesign initiatives to enhance their performance such as the program launched in 2001, “Crossing the Quality Chasm”( 1 ). These strategic changes are driven by the need to address the evolving healthcare landscape, improve patient outcomes, and ensure equitable access to healthcare services, in order to take into account primarily the following aspects: (i) enhance patient-centred care to engage patients in their healthcare decisions while improving their overall health experience ( 2 ) ( 3 ), (ii) primary care strengthening interventions to fortify the whole healthcare system ( 4 ), (iii) reduce healthcare disparities through a broader access to healthcare services ( 5 ) ( 6 ), (iv) addressing the determinants of health through the implementation of health educational activities ( 7 ) ( 8 ), (v) developing emergency preparedness in case of major events, healthcare crisis ( 9 ), (vi) workforce policies crafting in order to enhance their well-being and job satisfaction to counter growing phenomena such as healthcare personnel attrition( 10 ) ( 11 ). The promotion of organizational models that allow the appropriateness and customization of care delivery in the community is a topic that has become increasingly important in healthcare management in recent years, especially after the pandemic that highlighted the grave consequences of their unavailability( 12 ). Strengthening primary health care and community care is considered a foundation for high-quality health systems ( 13 ) and it supports the universal health coverage which is a specific indicator of the Sustainable Development Goals (SDGs) ( 14 ) number 3: “Good health and wellbeing”. In particular, innovative care models are needed to shift the centre of gravity from the hospital to the community care and realise their two interdependent outcomes( 15 ), namely achieving the best quality of care and patient safety and personnel’ satisfaction. Many strategies and policies have been proposed to reinforce primary healthcare and community care such as the Chronic Care Model ( 16 ) the Expanded Chronic Care Model ( 17 ), self-management ( 18 ), and the Family and Community Nurse (FCN)( 19 , 20 ). Although following sometimes different principles, during the pandemic, the FCN model has been implemented in Italy to tackle the pandemic ( 21 ) and it was cited in the Italian government acts such as the reform (DM77/2022) of community healthcare. Indeed, the FCN model is significantly fitting the Next Generation EU (NGEU) initiative, which is meant to provide financial support to all member states to recover from the adverse effects of the pandemic based on a plan of reforms and investments for the 2021–2026 period. Italy presented the “Recovery and Resilience Plan” ( 22 ), accepted by the European Commission, in which new forms of healthcare models are presented. In particular, the Italian regions have large autonomy in organizing and delivering care because of the decentralization of powers ( 23 ). Tuscany is the region with the highest number of FCN ( 24 ). This study focuses on the evaluation of the introduction of the innovative FCN care model in Tuscany measured through the nurses’ intention to leave (ITL). This term refers to the nurse's expressed desire or intention to leave their current job or position. This measure can be used to gauge the job satisfaction and retention levels among nurses. Nurses’ ITL can be influenced by a variety of factors, including job dissatisfaction, burnout, low morale, inadequate working condition ( 25 ). We focused on these aspects because extant evidence tells us that (26, 27 ) nurses that work in an innovation-prone organization are more satisfied and are more likely to keep their job and recommend it to others, hence their motivation is high: with these premises, the model is more likely to work and the system is less likely to experiment nursing shortage. This is in line with Caers ( 28 ), who emphasized that "job satisfaction is one of the strongest predictors of intent to stay and retention of nurses”. Considering the alarming circumstance of the nurse’s shortage, to be able to retain professionals at work is of primary relevance. A recent review( 19 ) shows that the FCN organizational model can have a positive impact on job satisfaction, thanks to its vision of the professional, who is holistically working through the continuity of care, actively involving patients, families, and communities in educational programmes. Methods The study setting is Tuscany, where the FCN model was deliberated by the Region in 2018 as a strong point of the Expanded Chronic Care Model ( 17 ) (i.e., measurable outputs of the healthcare system, self-management and self-decision, focus on teamwork, effective information systems and communities’ organizations partnership constitution, support communities towards being healthy). It is designed to create valuable outcomes for the citizens and for the Healthcare Organizations, as the healthcare offer is planned to occur “within the context of service user’s wider life experience”( 29 ). We suggest that this model moves towards the co-production of services not only in the sense of Osborne’ service framework ( 30 ) -that is focusing on the production of external value in addition to internal efficiency- but also in the direction of the reduction of disparities among population groups ( 31 ), using a “representative co-production” approach. Box 1- Main organizational features of the FNC’s model. Data were gathered from the Regional Organizational Climate Survey (OCS)( 32 ), a voluntary participation survey administered to all the components of the regional healthcare System every two years by the Scuola Superiore Sant’Anna. The OC is a census survey administered through C.A.W.I. (Computer Assisted Web Interview) invitation by e-mail and access via a dedicated page to the healthcare workers. The questionnaire includes more than 80 questions relating to various dimensions of work (e.g., working conditions, communication and information, the budget system etc.). We isolated the Local Health Authorities (LHAs), then nurses, and finally FNCsc ( 32 , 27 ). The population of Tuscan FCNs is constituted of 1078 units. Among the many questions asked, we isolated the four items that make up the intention to leave construct( 33 ): (i)” I often think about changing my job”, (ii) “I will probably look for a new job over the course of the next year” (iii) “I will leave my Company as soon as possible”, (iv) “I will transfer to another Department or Unit within my Company as soon as possible”. All items allow respondents to answer based on their level of agreement with the provided statements. The degree of agreement/disagreement is measured on a Likert scale from 1 to 5. In our analysis, we grouped the positive polarization 1 and 2, respectively totally disagree and disagree (nurses want to stay) and the negative polarization of people responding 4 and 5, respectively totally agree and agree (nurses want to leave). The 'neutral' group is composed of individuals who chose the value of 3. The analysis was conducted by comparing the FCNs’ responses with those of other nurses working in the community health services, who are not FCNs. The total number of respondents FCNs from both the LHAs that have implemented the model since 2019 was 297, whilst the number of nurses working in the other community health services was 1,007. Results As reported in Table 1 , a general decrease was registered in all ITL dimensions, meaning an improved intention to stay among the responding nursing personnel. Differences within the region immediately appear. On the positive side of the existence of variability, we mention the existence of a commitment to flexibility, enabling healthcare providers to adapt to changing community dynamics and healthcare demands. Table 1 Descriptive statistics and chi-square test on the data gathered from 2023’s OCS. Overall LHA1 LHA2 No FCN FCN Chi2 No FCN FCN Chi2 No FCN FCN Chi2 I often think about changing my job Positive 55% 61% 0.073 52% 61% 0.037 56% 60% 0.583 Neutral 18% 19% 18% 18% 20% 21% Negative 26% 20% 31% 21% 24% 19% I will probably look for a new job over the course of the next year Positive 79% 81% 0.245 77% 83% 0.144 80% 78% 0.537 Neutral 12% 12% 12% 11% 11% 15% Negative 9% 7% 10% 6% 8% 7% I will leave my company as soon as possible Positive 77% 82% 0.096 77% 82% 0.257 77% 80% 0.510 Neutral 12% 10% 10% 9% 12% 12% Negative 11% 8% 12% 8% 11% 7% I will transfer to another department or unit within my company as soon as possible Positive 77% 81% 0.213 76% 83% 0.121 78% 78% 0.619 Neutral 13% 11% 14% 10% 12% 15% Negative 10% 7% 11% 7% 9% 7% LHA1 and LHA2 implemented the FCN model respectively at the beginning and at the end of 2019, and the differences in the expressed values are the projection of their uniqueness and of the different stage of model maturity. LHA2 experiences higher percentages of employees changing their minds about the ITL compared to LHA1 because the change they are undergoing is more disruptive as it is new. LHA1 experiences a minority of employees changing their minds about the ITL, as the change they are facing is considered less disruptive since it is already established, the model is now part of their normality. Discussion FCN model organizational configurations will be focused, amongst other things, in strengthening the community care offer. Our work remarks how structural policy redesign in healthcare systems can significantly impact nurses' intention to leave their positions, and, in this sense, Tuscany provides a case study to examine the variability in these effects. We can state that Nurses, in regions with enhanced working conditions, policies attentive to organizational innovation and a role that recognizes them a well-defined social function, as highlighted by the milestones of the model, tend to have higher job satisfaction and lower turnover intentions. The insights gained from examining the variability across Tuscany can inform community nursing policy at a broader, national level. Structural policy redesign affects the intentions of FCNs to leave their roles differently throughout Tuscany, emphasizing the significance of tailored, region-specific strategies to enhance community nurse retention and overall well-being. Varied implementation fosters a culture of innovation, encouraging the exploration of effective nursing practices that may benefit other regions, even if at the same time may lead to inconsistencies in healthcare quality and patient experience, which will need to be further investigated. In the perspective of a model scaling, one aspect that must be taken into consideration is that the lack of standardized implementation in nursing care delivery models across communities in a specific territory may result in challenges related to data comparability, hindering the assessment of overall healthcare system performance. Declarations Ethics approval : Ethics approval is deemed unnecessary according to national regulations: art. 14, comma 5 del D.Lgs 150/09 and to Delibera della Regione Toscana N.91/2023. Consent to participate: Informed consent to participate was obtained from all of the participants in the form of voluntary participation to the questionnaire. Explicit consent, based on the Delibera della Regione Toscana N.91/2023. Consent for publication: Explicit consent gathered after the explanation, context, and the purpose of the investigation, based on the Delibera della Regione Toscana N.91/2023. Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: None declared Fundings : This study originates from funding provided by the Tuscany Region aimed at a comprehensive examination of the organizational model of family and community nurses. The funder has no specific role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. Authors' contributions: MV, CB and LT conceived and designed the study. MV led the data analysis, CB drafted the paper. LT contributed to the data analysis. All authors read drafts of the manuscript and approved the final version. Acknowledgements: The authors wish to thank Paolo Zoppi, Andrea Lenzini and Vianella Agostinelli. Moreover, the authors are grateful to the researchers of Management and Health Laboratory for their help in collecting data, specifically Nicola Bellè, Paola Cantarelli and Luca Pirrotta. References Baker A. Crossing the quality chasm: a new health system for the 21st century. Volume 323. British Medical Journal Publishing Group; 2001. Delaney LJ. Patient-centred care as an approach to improving health care in Australia. Collegian. 2018;25(1):119–23. Wong E, Mavondo F, Fisher J. Patient feedback to improve quality of patient-centred care in public hospitals: a systematic review of the evidence. BMC Health Serv Res. 2020;20:1–17. Beard JR, Officer A, De Carvalho IA, Sadana R, Pot AM, Michel J-P, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet. 2016;387(10033):2145–54. Braveman PA, Kumanyika S, Fielding J, LaVeist T, Borrell LN, Manderscheid R, et al. Health disparities and health equity: the issue is justice. 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Nord Sch. 2015;69. Osborne SP. Public service logic: creating value for public service users, citizens, and society through public service delivery. Routledge; 2020. Eriksson EM. Representative co-production: broadening the scope of the public service logic. Public Manag Rev. 2019;21(2):291–314. Pizzini S, Furlan M. L’esercizio delle competenze manageriali e il clima interno. Il caso del Servizio Sanitario della Toscana. Psicol Soc. 2012;7(3):429–46. OECD. Engaging public employees for a high-performing civil service. OECD Publishing Paris; 2016. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3815418","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264617237,"identity":"944c6761-265d-42f7-9df3-4f186188044a","order_by":0,"name":"chiara barchielli","email":"data:image/png;base64,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","orcid":"","institution":"Sant'Anna School of Advanced Studies","correspondingAuthor":true,"prefix":"","firstName":"chiara","middleName":"","lastName":"barchielli","suffix":""},{"id":264617238,"identity":"faca68fe-08de-4307-a7f1-946caca6f93c","order_by":1,"name":"milena vainieri","email":"","orcid":"","institution":"Sant'Anna School of Advanced Studies","correspondingAuthor":false,"prefix":"","firstName":"milena","middleName":"","lastName":"vainieri","suffix":""},{"id":264617239,"identity":"7d57a15e-8b71-45b1-8ba6-360396d9cbd2","order_by":2,"name":"lorenzo taddeucci","email":"","orcid":"","institution":"Sant'Anna School of Advanced Studies","correspondingAuthor":false,"prefix":"","firstName":"lorenzo","middleName":"","lastName":"taddeucci","suffix":""}],"badges":[],"createdAt":"2023-12-28 07:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3815418/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3815418/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51283444,"identity":"d77ebfaa-a888-4edd-9efb-2f05aae8e19e","added_by":"auto","created_at":"2024-02-18 10:37:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":354786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3815418/v1/ef5c573d-977c-4fa2-8385-019b693b46d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When organizational models influence the intention to leave of professionals. The case of Family and Community Nurses in the Tuscan healthcare system","fulltext":[{"header":"Strengths and limitations of this study","content":"\u003cul\u003e\n \u003cli\u003eThe real-world evidence reported are the first reliable assessment of the ITL among Family and Community Nurses and Nurses in Italy.\u003c/li\u003e\n \u003cli\u003eTuscany has been a field for a natural experiment, a major healthcare structural policy redesign.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAlthough the sample size and the evidence retrieved, it is a regional study, hence results cannot be scalable.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003ePeriodically, health systems undertake specific policies and structural redesign initiatives to enhance their performance such as the program launched in 2001, \u0026ldquo;Crossing the Quality Chasm\u0026rdquo;(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These strategic changes are driven by the need to address the evolving healthcare landscape, improve patient outcomes, and ensure equitable access to healthcare services, in order to take into account primarily the following aspects: (i) enhance patient-centred care to engage patients in their healthcare decisions while improving their overall health experience (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), (ii) primary care strengthening interventions to fortify the whole healthcare system (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), (iii) reduce healthcare disparities through a broader access to healthcare services (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), (iv) addressing the determinants of health through the implementation of health educational activities (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), (v) developing emergency preparedness in case of major events, healthcare crisis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), (vi) workforce policies crafting in order to enhance their well-being and job satisfaction to counter growing phenomena such as healthcare personnel attrition(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The promotion of organizational models that allow the appropriateness and customization of care delivery in the community is a topic that has become increasingly important in healthcare management in recent years, especially after the pandemic that highlighted the grave consequences of their unavailability(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Strengthening primary health care and community care is considered a foundation for high-quality health systems (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and it supports the universal health coverage which is a specific indicator of the Sustainable Development Goals (SDGs) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) number 3: \u0026ldquo;Good health and wellbeing\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIn particular, innovative care models are needed to shift the centre of gravity from the hospital to the community care and realise their two interdependent outcomes(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), namely achieving the best quality of care and patient safety and personnel\u0026rsquo; satisfaction.\u003c/p\u003e \u003cp\u003eMany strategies and policies have been proposed to reinforce primary healthcare and community care such as the Chronic Care Model (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) the Expanded Chronic Care Model (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), self-management (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and the Family and Community Nurse (FCN)(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although following sometimes different principles, during the pandemic, the FCN model has been implemented in Italy to tackle the pandemic (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and it was cited in the Italian government acts such as the reform (DM77/2022) of community healthcare. Indeed, the FCN model is significantly fitting the Next Generation EU (NGEU) initiative, which is meant to provide financial support to all member states to recover from the adverse effects of the pandemic based on a plan of reforms and investments for the 2021\u0026ndash;2026 period. Italy presented the \u0026ldquo;Recovery and Resilience Plan\u0026rdquo; (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), accepted by the European Commission, in which new forms of healthcare models are presented.\u003c/p\u003e \u003cp\u003eIn particular, the Italian regions have large autonomy in organizing and delivering care because of the decentralization of powers (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Tuscany is the region with the highest number of FCN (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study focuses on the evaluation of the introduction of the innovative FCN care model in Tuscany measured through the nurses\u0026rsquo; intention to leave (ITL). This term refers to the nurse's expressed desire or intention to leave their current job or position. This measure can be used to gauge the job satisfaction and retention levels among nurses. Nurses\u0026rsquo; ITL can be influenced by a variety of factors, including job dissatisfaction, burnout, low morale, inadequate working condition (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We focused on these aspects because extant evidence tells us that (26, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) nurses that work in an innovation-prone organization are more satisfied and are more likely to keep their job and recommend it to others, hence their motivation is high: with these premises, the model is more likely to work and the system is less likely to experiment nursing shortage. This is in line with Caers (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), who emphasized that \"job satisfaction is one of the strongest predictors of intent to stay and retention of nurses\u0026rdquo;. Considering the alarming circumstance of the nurse\u0026rsquo;s shortage, to be able to retain professionals at work is of primary relevance. A recent review(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) shows that the FCN organizational model can have a positive impact on job satisfaction, thanks to its vision of the professional, who is holistically working through the continuity of care, actively involving patients, families, and communities in educational programmes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study setting is Tuscany, where the FCN model was deliberated by the Region in 2018 as a strong point of the Expanded Chronic Care Model (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) (i.e., measurable outputs of the healthcare system, self-management and self-decision, focus on teamwork, effective information systems and communities\u0026rsquo; organizations partnership constitution, support communities towards being healthy). It is designed to create valuable outcomes for the citizens and for the Healthcare Organizations, as the healthcare offer is planned to occur \u0026ldquo;within the context of service user\u0026rsquo;s wider life experience\u0026rdquo;(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). We suggest that this model moves towards the co-production of services not only in the sense of Osborne\u0026rsquo; service framework (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) -that is focusing on the production of external value in addition to internal efficiency- but also in the direction of the reduction of disparities among population groups (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), using a \u0026ldquo;representative co-production\u0026rdquo; approach.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" style=\"width: 737px;\"\u003e\u003cbr\u003e\u003c/p\u003e \u003cp\u003eBox 1- Main organizational features of the FNC\u0026rsquo;s model.\u003c/p\u003e \u003cp\u003eData were gathered from the Regional Organizational Climate Survey (OCS)(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), a voluntary participation survey administered to all the components of the regional healthcare System every two years by the Scuola Superiore Sant\u0026rsquo;Anna. The OC is a census survey administered through C.A.W.I. (Computer Assisted Web Interview) invitation by e-mail and access via a dedicated page to the healthcare workers. The questionnaire includes more than 80 questions relating to various dimensions of work (e.g., working conditions, communication and information, the budget system etc.). We isolated the Local Health Authorities (LHAs), then nurses, and finally FNCsc (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The population of Tuscan FCNs is constituted of 1078 units. Among the many questions asked, we isolated the four items that make up the intention to leave construct(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e): (i)\u0026rdquo; I often think about changing my job\u0026rdquo;, (ii) \u0026ldquo;I will probably look for a new job over the course of the next year\u0026rdquo; (iii) \u0026ldquo;I will leave my Company as soon as possible\u0026rdquo;, (iv) \u0026ldquo;I will transfer to another Department or Unit within my Company as soon as possible\u0026rdquo;. All items allow respondents to answer based on their level of agreement with the provided statements. The degree of agreement/disagreement is measured on a Likert scale from 1 to 5. In our analysis, we grouped the positive polarization 1 and 2, respectively totally disagree and disagree (nurses want to stay) and the negative polarization of people responding 4 and 5, respectively totally agree and agree (nurses want to leave). The 'neutral' group is composed of individuals who chose the value of 3. The analysis was conducted by comparing the FCNs\u0026rsquo; responses with those of other nurses working in the community health services, who are not FCNs. The total number of respondents FCNs from both the LHAs that have implemented the model since 2019 was 297, whilst the number of nurses working in the other community health services was 1,007.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAs reported in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, a general decrease was registered in all ITL dimensions, meaning an improved intention to stay among the responding nursing personnel. Differences within the region immediately appear. On the positive side of the existence of variability, we mention the existence of a commitment to flexibility, enabling healthcare providers to adapt to changing community dynamics and healthcare demands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and chi-square test on the data gathered from 2023\u0026rsquo;s OCS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eLHA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eLHA2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo FCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo FCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNo FCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eFCN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eChi2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eI often think about changing my job\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eI will probably look for a new job over the course of the next year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eI will leave my company as soon as possible\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eI will transfer to another department or unit within my company as soon as possible\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePositive\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNeutral\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNegative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLHA1 and LHA2 implemented the FCN model respectively at the beginning and at the end of 2019, and the differences in the expressed values are the projection of their uniqueness and of the different stage of model maturity. LHA2 experiences higher percentages of employees changing their minds about the ITL compared to LHA1 because the change they are undergoing is more disruptive as it is new. LHA1 experiences a minority of employees changing their minds about the ITL, as the change they are facing is considered less disruptive since it is already established, the model is now part of their normality.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFCN model organizational configurations will be focused, amongst other things, in strengthening the community care offer. Our work remarks how structural policy redesign in healthcare systems can significantly impact nurses' intention to leave their positions, and, in this sense, Tuscany provides a case study to examine the variability in these effects. We can state that Nurses, in regions with enhanced working conditions, policies attentive to organizational innovation and a role that recognizes them a well-defined social function, as highlighted by the milestones of the model, tend to have higher job satisfaction and lower turnover intentions.\u003c/p\u003e \u003cp\u003eThe insights gained from examining the variability across Tuscany can inform community nursing policy at a broader, national level. Structural policy redesign affects the intentions of FCNs to leave their roles differently throughout Tuscany, emphasizing the significance of tailored, region-specific strategies to enhance community nurse retention and overall well-being. Varied implementation fosters a culture of innovation, encouraging the exploration of effective nursing practices that may benefit other regions, even if at the same time may lead to inconsistencies in healthcare quality and patient experience, which will need to be further investigated. In the perspective of a model scaling, one aspect that must be taken into consideration is that the lack of standardized implementation in nursing care delivery models across communities in a specific territory may result in challenges related to data comparability, hindering the assessment of overall healthcare system performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: Ethics approval is deemed unnecessary according to national regulations: art. 14, comma 5 del D.Lgs 150/09 and to Delibera della Regione Toscana N.91/2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent to participate:\u003c/strong\u003e Informed consent to participate was obtained from all of the participants in the form of voluntary participation to the questionnaire. Explicit consent, based on the Delibera della Regione Toscana N.91/2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eExplicit consent gathered after the explanation, context, and the purpose of the investigation, based on the Delibera della Regione Toscana N.91/2023.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e: This study originates from funding provided by the Tuscany Region aimed at a comprehensive examination of the organizational model of family and community nurses. The funder has no specific role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eMV, CB and LT conceived and designed the study. MV led the data analysis, CB drafted the paper. LT contributed to the data analysis. All authors read drafts of the manuscript and approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors wish to thank Paolo Zoppi, Andrea Lenzini and Vianella Agostinelli. Moreover, the authors are grateful to the researchers of Management and Health Laboratory for their help in collecting data, specifically Nicola Bell\u0026egrave;, Paola Cantarelli and Luca Pirrotta.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaker A. 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Health Aff. 2009;28(1):75\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarr V, Robinson S, Marin-Link B, Underhill L, Dotts A, Ravensdale D, et al. The expanded chronic care model. Hosp Q. 2003;7(1):73\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBodenheimer T, Lorig K, Holman H, Grumbach K. Patient self-management of chronic disease in primary care. JAMA. 2002;288(19):2469\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDellafiore F, Caruso R, Cossu M, Russo S, Baroni I, Barello S, et al. The state of the evidence about the family and community nurse: a systematic review. Int J Environ Res Public Health. 2022;19(7):4382.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHennessy D, Gladin L, Organization WH. Report on the evaluation of the WHO multi-country family health nurse pilot study. Copenhagen: WHO Regional Office for Europe; 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarisone M, Busca E, Bassi E, De Luca E, Profenna E, Suardi B, et al. The Family and Community Nurses Cultural Model in the Times of the COVID Outbreak: A Focused Ethnographic Study. Int J Environ Res Public Health. 2023;20(3):1948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItaliano G. Piano Nazionale di Ripresa e Resilienza (PNRR). Trasmissione del PNRR al Parlamento; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaltman R, Busse R, Figueras J. Decentralization in health care: strategies and outcomes. McGraw-hill education (UK); 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartini L, Addesso D, Di Falco A, Costa C, Mantoan D. Family nurses in Italy: an explorative survey. 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Heal Serv Manag Res. 2020;0951484820943596.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaers R, Du Bois C, Jegers M, De Gieter S, De Cooman R, Pepermans R. Measuring community nurses\u0026rsquo; job satisfaction: literature review. J Adv Nurs. 2008;62(5):521\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGr\u0026ouml;nroos C, Strandvik T, Heinonen K. Value co-creation: Critical reflections. Nord Sch. 2015;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsborne SP. Public service logic: creating value for public service users, citizens, and society through public service delivery. Routledge; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEriksson EM. Representative co-production: broadening the scope of the public service logic. Public Manag Rev. 2019;21(2):291\u0026ndash;314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePizzini S, Furlan M. L\u0026rsquo;esercizio delle competenze manageriali e il clima interno. Il caso del Servizio Sanitario della Toscana. Psicol Soc. 2012;7(3):429\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOECD. Engaging public employees for a high-performing civil service. OECD Publishing Paris; 2016.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intention to leave, Nurses, Organizational model, healthcare restructuring policy/intervention","lastPublishedDoi":"10.21203/rs.3.rs-3815418/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3815418/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo describe Nurses\u0026rsquo; Intention to Leave (ITL) their job across Tuscany -Italy- after a major healthcare structural policy redesign of the community organizational care delivery model, namely the Family and Community Nurse (FCN) model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this organizational case study, we evaluate the differences in the effects on Nurses\u0026rsquo; ITL through the comparison between non-FCNs and FCN\u0026rsquo;s answers to the region-wide administered Organizational Climate Survey (OCS) conducted in 2023, after the model\u0026rsquo;s implementation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA general decrease in all ITL dimensions was registered, indicating, therefore, a higher FCNs\u0026rsquo; intention to stay. There are variations in the answers from Nurses working in different Local Health Authorities (LHAs), and it is due to the uniqueness of organizations and to the dissimilar levels of maturity of the organizational models within them.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe effects of structural policy redesign on Nurses\u0026rsquo; ITL highlight the importance of tailored, region-specific approaches to improve Nurses\u0026rsquo; retention in the healthcare sector, which is currently experimenting the worst Nursing shortage crisis ever.\u003c/p\u003e","manuscriptTitle":"When organizational models influence the intention to leave of professionals. The case of Family and Community Nurses in the Tuscan healthcare system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 08:36:30","doi":"10.21203/rs.3.rs-3815418/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"99069f28-1cc7-4f9c-8af7-b0b909aacedb","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-21T15:44:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 08:36:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3815418","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3815418","identity":"rs-3815418","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}