Introduction
Cardiovascular disease (CVD) is a public health priority with fatal and debilitating consequences affecting millions of people each year (Adhikary et al. Reference Adhikary, Barman, Ranjan and Stone2022; Ramic-Catak et al. Reference Ramic-Catak, Mesihović-Dinarevic and Prnjavorac2023; Irish Heart Foundation 2024). A 2020 study investigating global burden of disease estimated that 523 million people had CVDs, and that 18.6 million people died due to CVD in 2019 (Roth et al. Reference Roth, Mensah, Johnson, Addolorato, Ammirati and Baddour2020). Considerable burden has also been estimated in terms of disability-adjusted life years (Roth et al. Reference Roth, Mensah, Johnson, Addolorato, Ammirati and Baddour2020). Common cardiovascular diseases include coronary artery disease, stroke, heart failure, atrial fibrillation and peripheral arterial disease. Key risk factors encompass health behaviours (such as diet, physical activity, and smoking), elevated blood pressure, diabetes mellitus and cholesterol, as well as socio-environmental and healthcare-related factors (Joseph et al. Reference Joseph, Leong, McKee, Anand, Schwalm, Teo, Mente and Yusuf2017).
There is a strong relationship between CVD and mental health (Levine et al. Reference Levine, Cohen, Commodore-Mensah, Fleury, Huffman, Khalid, Labarthe, Lavretsky, Michos and Spatz2021). This is related to the role of social and environmental factors and health behaviours and their health impacts (Adler Reference Adler2009; Kilanowski Reference Kilanowski2017; Noar and Zimmerman Reference Noar and Zimmerman2005; Boehm and Kubzansky Reference Boehm and Kubzansky2012; Chen and Miller Reference Chen and Miller2012). People experiencing mental disorders have an increased CVD risk (Nielsen et al. Reference Nielsen, Banner and Jensen2021; Minhas et al. Reference Minhas, Patel, Malik, Hana, Hassan and Khouzam2022). A recent study supports the co-existence of mental disorders and CVD due to common pathogenic mechanisms (Parlati et al. Reference Parlati, Nardi, Basile, Paolillo, Marzano and Chirico2024), and a 2017 meta-analysis indicated that 9.9% of people with severe mental illness (SMI) had CVD and were at greater risk of developing CVD compared to the general population. Still, people with mental health disorders (MHDs) have trouble accessing CVD screening or treatments (Solmi et al. Reference Solmi, Fiedorowicz, Poddighe, Delogu, Miola and Høye2021). Possible explanations for this include insufficient physical health assessments, low confidence among mental health professionals in prescribing physical health medications, busy general practice workloads, and high levels of mental health illiteracy/stigma among healthcare providers (Solmi et al. Reference Solmi, Fiedorowicz, Poddighe, Delogu, Miola and Høye2021).
There is growing recognition that structured programmes identifying people with high CVD risk and addressing risk factors through health behaviour change and pharmacological intervention can optimise population health. Since 2020, Ireland’s ‘Chronic Disease Management Programme’ (CDM) has ensured that people with CVD (as well as chronic respiratory disease and diabetes) can access regular structured reviews in general practice. Further, an additional group of patients are eligible to participate in the ‘Chronic Disease Management Prevention Programme’ (PP), whereby they can also access annual general practice reviews, medication reviews, planning to help manage risk factors, health promotion advice, appropriate medical treatment, and support service referrals. The opportunistic case finding (OCF) programme explicitly notes SMI as a reason for case finding for cardiometabolic risk. However, a significant proportion of patients are excluded from these programmes due to ineligibility for a medical card or doctor visit card. This exclusion can exacerbate inequities in access to CDM (Linnane et al. Reference Linnane, Mullarkey, Kyne, Healy, Fallon, Sharma, Hannigan, O’Regan and O’Connor2025).
General practice is therefore well placed to address CVD risk. Previous research by our team in collaboration with the Irish Heart Foundation showed a ‘High-Risk Prevention Programme’ was feasible, acceptable and likely effective in addressing cardiovascular risk among high-risk patients (Broughan et al. Reference Broughan, Sietiņš, Treanor, Siu, Morrissey, Doyle, Casey, Fitzpatrick, McCombe and Cullen2024). Although people with MHDs have a high CVD risk, they are less likely to access CVD care (Irish Heart Foundation 2019). The Mental Health Commission’s report on ‘Physical health of people with severe mental illness’ highlights increased risk of physical health issues including CVD risk in this population and the need for integrated care and monitoring (Mental Health Commission 2019). This position is supported by several theoretical frameworks emphasising the importance of integrated care within community settings including the ‘Primary Care Behavioural Health Model’, ‘Comprehensive Healthcare Integration Framework’, and ‘Integrated Community Framework’ (Reiter et al. Reference Reiter, Dobmeyer and Hunter2018; Chung et al. Reference Chung, Parks, Minkoff and Raney2023; Thiam et al. Reference Thiam, Allaire, Morin, Hyppolite, Dore, Zomahoun and Garon2021).
However, there are still large knowledge gaps regarding CVD risk management for general practice patients with MHDs, and interventions that might improve CVD prevention. For the purpose of this study, “general practice patients with mental health disorders” includes both individuals with MHDs who are managed solely within general practice, and those who attend specialist mental health services (e.g. psychiatrists, NCHDs) but also visit their GP for physical health concerns such as cardiovascular risk factors and other comorbidities. These two groups have different care structures and levels of clinical oversight, which impacts how roles and responsibilities for CVD risk assessment are defined and coordinated. Accordingly, this paper explores the perspectives and views of the healthcare professionals in general practice on how CVD risk is assessed and managed in this population.
This study aimed to explore general practice professionals’ perspectives on CVD assessment for patients with MHDs in general practice and to describe current practice-level approaches to identifying this population using the Mental Health Finder (MHF) tool.
Objectives
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To describe practice-level data regarding the use of electronic tools to identify patients with MHDs and their enrolment in national CDMs.
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To understand how common are MHDs in general practice, to explore the perspectives on association between MHDs and CVD, and experiences of general practice professionals regarding CVD risk management for patients with MHDs.
Methods
Study setting, participants and sampling
Anonymised, aggregated data on patients with MHDs were requested from six practices recruited through the UCD / Ireland East GP Research Network (a Practice-Based Research Network established in collaboration between the UCD School of Medicine and Ireland East Hospital Group) to assess the feasibility of data collection for future studies (UCD School of Medicine 2024). Practices were also surveyed on the availability of electronic data collection tools, specifically the MHF tool, a software plug-in developed for the Socrates electronic medical record (EMR) system, in collaboration with the Irish Primary Care Research Network. It is designed to support the identification of patients with mental health conditions by automatically searching EMR data based on a broad set of diagnostic codes for MHDs (e.g. ICD-10 codes for anxiety and depression) and/or records of prescribed medications typically used to treat these conditions (e.g. SSRIs, antipsychotics) (Swan et al. Reference Swan, Hannigan, Higgins, McDonnell, Meagher and Cullen2017).
In-depth qualitative interviews were conducted with 12 General Practitioners (GPs) and three General Practice Nurses (GPNs). Research suggests that sample sizes in the range of 12 participants may be sufficient to achieve data saturation in qualitative studies which will be considered when reaching the point where further data collection becomes ‘counter-productive’, and where the ‘new’ does not necessarily add anything to the overall study or theory (Hennink and Kaiser Reference Hennink and Kaiser2022). Invitations to participate were sent to seven practices selected through the UCD/Ireland East GP Research Network. These invitations included detailed study information and an informed consent form to be returned by post or email. Practices that consented to participate were then asked to invite GPs and GPNs from their teams to take part in the study. Through this network, five GPs and two GPNs were recruited
Additionally, a purposive sample of healthcare professionals from four further practices was recruited via the teaching networks of the Department of General Practice at the UCD School of Medicine. From these practices, seven GPs and one GPN were recruited. Informed consent was obtained from all participating GPs and GPNs, along with their contact information, and interviews were subsequently scheduled.
Methodological approach and research paradigm
This study adopted an embedded mixed-methods approach (Ahmed et al. Reference Ahmed, Pereira and Jane2024). Aggregated anonymised data provided contextual information regarding the identification of patients with MHDs and participation in chronic disease programmes. The qualitative component explored the perspectives of general practice professionals regarding CVD risk assessment in this population. Although the two components were analysed separately, their findings were interpreted together to provide complementary insights, aligning with an embedded mixed-methods framework.
The qualitative component was guided by a constructivist grounded theoretical framework complemented by the Social Ecological Model (SEM). Using a constructivist grounded theory approach means that researchers and participants work together to build an understanding of people’s experiences, rather than assuming meaning beforehand (Chun Tie et al. Reference Chun Tie, Birks and Francis2019). Data collection and analysis happen at the same time, with researchers continually comparing new information to what has already been gathered. Through this ongoing comparison, patterns and categories gradually take shape and are refined (Chun Tie et al. Reference Chun Tie, Birks and Francis2019). The SEM was used alongside this approach to help the researchers look at influences on participants’ experiences at several levels not just individual factors, but also relationships, community settings, and wider policies (Eriksson et al. Reference Eriksson, Ghazinour and Hammarström2018). This framework allowed the authors to explore how people make sense of their experiences while also considering the broader context that shapes those experiences (Podgorski et al. Reference Podgorski, Anderson and Parmar2021).The study’s method was guided by the Standards for Reporting Qualitative Research (SRQR) guidelines (O’Brien et al. Reference O’Brien, Harris, Beckman, Reed and Cook2014).
Quantitative data collection and analysis
Aggregated anonymised data were collected from five of the six participating general practices (83.3%). One practice did not return the data due to administrative constraints. These data were obtained through the EMR system included the availability of the MHF tool; the total number of registered patients per practice; the number of patients identified by the tool; and the numbers of patients enrolled in the CDM, OCF and PP initiatives. The total number and proportion of GMS-eligible patients in each practice were also recorded.
The purpose of collecting these aggregated data was to provide contextual information regarding the use of MHF tool and chronic disease programmes within general practice and to assess the feasibility of identifying patients with MHDs for cardiovascular risk assessment. No individual-level demographic or clinical data (e.g. age, gender, diagnosis) were extracted. Data were analysed using SPSS and presented in frequencies and percentages.
Qualitative data collection and analysis
Semi-structured interviews with the participants were conducted over telephone / zoom audio by NR from 08/04/2025 to 26/06/2025. Interviews were audio recorded and transcribed. Transcripts were pseudonymised prior to analysis to ensure confidentiality. All electronic data were stored on encrypted files (Word and NVivo 12) and computers belonging to research team.
A topic guide used for the semi-structured interviews is available in the Supporting file Appendix (S1. Appendix 1 – Topic guide).
The interview transcripts were analysed by authors NR and YX and audited by the author JB. NVivo v12 software was used to store the transcribed interviews, perform data analysis identifying quotes for the relevant feasibility themes, and develop the coding scheme by which data were categorised into their respective themes. All members of the research team maintained reflexivity throughout data analysis by having regular meetings in which identified themes were discussed and when necessary, revised to reflect the various components of the model.
Acceptability and feasibility of the intervention were assessed using the reflexive thematic analysis approach designed by Braun and Clark (Braun and Clarke Reference Braun and Clarke2006). The reflexive thematic analysis enabled us to use a data-driven/bottom-up approach in analysing the transcripts to find themes that arise from the interviews with study participants.
Results
Anonymised aggregated data (N = 5 practices)
Among the five practices providing the anonymised data, only two had access to the MHF tool. Two additional practices had diagnostic coding in their practice software, but it captured either no mental health conditions or only a few. Identification of mental health patients is higher in practices with the MHF tool. Practices using the tool reported a combined mental health prevalence of 18.7%, compared to lower apparent identification rates ranging from 0.5% to 11.5% in practices without the tool. The average percentage of GMS patients across the practices was 41.9%. Of the GMS patients, 21.3% were identified for CDM, 15.8% for Prevention programme (PP) and 10.7% for OCF programmes. CDM participation is the highest among all programmes (21.3%), suggesting strong CDM engagement (see Table 1).
Anonymised aggregated data (n = 5 practices)
Bold indicates the prevalence in practices with data tool available and underline indicates the underdiagnose in practices with data tool not available.
* Underdiagnosis due to lack of data collection tools.
Qualitative analysis (n = 15)
Fifteen participants (12 GPs and 3 GPNs) took part in the in-depth semi-structured interviews. Of these, ten participants (66.7%) were female and five (33.3%) were male. Eleven participants (73.3%) were from urban practices, while the remaining four (26.7%) represented rural and mixed practices. The practices were located across four counties: Dublin, Meath, Wicklow, and Kilkenny.
Using the thematic analysis approach, four themes were identified from the interviews with participants (Figure 1).
Themes identified on GPs and GPNs interviews (n = 15).
Theme 1: Prevalence of MHD in general practice
All participants reported encountering patients with MHDs in their practice frequently. While most of the participants estimated the prevalence at around 20%, others reported prevalence rates ranging from 30-50%. One participant noted that MHDs are underdiagnosed in general practice settings. This under-recognition may reflect limited consultation time, diagnostic uncertainty, or avoidance of formal coding due to perceived stigma or patient preference. The finding illustrates how the true burden of MHDs in general practice may be underestimated in routinely collected data, reinforcing the value of tools such as the MHF.
Theme 2: Association between MHD and CVD risk
Most participants highlighted a significant association between CVD risk and MHDs, particularly SMIs such as schizophrenia and bipolar disorder.
While a few participants had not previously considered this association, others observed that the younger demographic typically presenting with MHDs often receive limited cardiovascular health assessment.
Participants commonly highlighted that patients with SMI such as bipolar and schizophrenia have a higher risk of CVD, noting the cardiometabolic side effects of antipsychotic medications but is not limited to severe depression and psychosis. Some noted that the risk may be more related to treatment than to diagnosis. Additional factors discussed included smoking, and lifestyle changes due to reduced motivation.
Together, these views reveal variability in awareness and clinical prioritisation. Practitioners were more likely to associate CVD risk with older or severely ill patients, suggesting a gap in recognising early cardiometabolic risk among younger or less severe MHD groups. This indicates a need for clearer guidance and training on systematic CVD risk consideration in mental health populations.
Theme 3: CVD risk management in patients with MHD in GP
CVD risk assessment
Most participants reported using the same approaches as those used with the general population to assess CVD risk in patients with MHD. These include Q-Risk estimation, assessment of circulating biomarkers (e.g. lipids, HbA1c) captured under national programmes namely CDM, OCF and PPs. However, they noted that risk assessment in this group tends to be opportunistic and not routinely prioritised unless concerns are raised.
Participants also referenced Mental Health Commission guidelines requiring annual metabolic assessments for patients receiving psychiatric secondary care. This illustrates the tension between policy expectations and everyday practice realities although clinicians recognise the importance of monitoring, system-level supports and reminders are lacking.
Identification of risk factors
CVD risk factors commonly identified in this population included smoking, obesity, diabetes, dyslipidaemia, and family history. Smoking and diabetes were particularly prevalent, with smoking often linked to its use as a coping mechanism and the impact of reduced motivation.
About half the participants reported that they do not routinely seek out CVD risk factors (e.g. lipids, glucose, blood pressure) in patients with MHD unless prompted by other concerns. Observable risk factors such as obesity and smoking were more likely to be noted opportunistically.
These accounts show how competing priorities in brief consultations lead clinicians to focus on acute mental health needs over preventive physical health care. Risk factor identification often depends on visible cues such as obesity or smoking, leaving silent risks undetected. The lack of systematic prompts or shared-care protocols appears to reinforce this reactive approach.
Treatment of risk factors
Participants stated they apply the same treatment approaches used with the general population to treat the patients with MHD. These approaches include lifestyle modifications, dietary changes and pharmacotherapy such as lipid management.
While this suggests equitable intentions, participants also acknowledged that patients with MHD face greater barriers to adherence and follow-up. This implies that “same treatment” may not achieve equal outcomes without additional support or tailored interventions addressing motivation, engagement, and socioeconomic context.
Theme 4: Holistic care
Challenges
Participants acknowledged that patients with MHD are a high-risk group and the identification of CVD risk factors in this population is inadequate. They noted that the CDM programme doesn’t actively capture this population.
Guidelines for CVD monitoring in secondary care are often funded, while similar monitoring in general practice remains ad hoc and unfunded. Barriers include pressure from secondary care, patients not attending general practice regularly, GP workload, and limited resources.
Patient-level barriers include poor compliance with follow-ups, lack of motivation resulting in poor engagement in health behaviour interventions, socioeconomic disadvantages, reduced healthcare access, and low health literacy. These findings underline the structural and relational barriers that constrain holistic care, clinicians feel responsible but under-resourced, creating tension between professional ideals and practical limitations.
Considerations
Participants recommended improved communication between general practice and secondary psychiatric care, particularly regarding cardiometabolic assessments and blood work. They suggested regular GP reviews including medication reviews for patients on antipsychotics, and the implementation of screening programmes to identify risk factors. Some of the participants highlighted that integrating psychiatric nurses into general practice was also considered beneficial for holistic care. Additional suggestions included outreach, incentivising patient participation, promoting self-management, and offering flexibility for disadvantaged groups. Some participants called for more research into the MHD–CVD association.
These ideas reflect recognition that relational continuity and flexibility are critical for engagement. They also highlight clinicians’ awareness of the broader social determinants including poverty, stigma, and mental health literacy that influence health behaviours and attendance.
Strategic interventions
Participants stressed the need for structured protocols for CVD assessment and treatment in this population. Suggestions included expanding the GMS criteria and incorporating mental health into the CDM programme. Currently, GMS eligibility includes individuals who are ordinarily resident in Ireland and who meet specific income thresholds, as well as all those aged over 70 years. Children under 6 years of age, and those under 16 years who receive the Domiciliary Care Allowance, may also qualify for a GMS card.
Structured educational programmes were also recommended, particularly for GPNs. All participants agreed that delivering PPs through key workers such as practice-based coordinators or community mental health nurses who maintain regular contact with patients and can support adherence to preventive care initiatives would be effective. They also supported extending the role of GPNs for this patient group.
These reflections link closely to the quantitative findings showing limited use of systematic data collection tool namely MHF in most practices. Together, they suggest that structural enablers such as expanding digital tools, creating shared protocols, and resourcing collaborative roles are key to implementing truly holistic, preventive care for patients with MHDs (see Table 2).
Themes identified and excerpts from the interviews
Discussion
Key findings
This study aimed to explore general practice professionals’ perspectives on CVD risk assessment for patients with MHDs, supported by aggregated data describing practice-level recording of relevant indicators. The aggregated anonymised data identified the prevalence of MHD in the participating general practices ranged between 18–20% whereas the qualitative component estimated the prevalence ranging between 30–50%. The low prevalence reported in the two practices without the MHF tool (0.5–11.5%) highlights the value of data collection tools that are embedded within practice management systems to identify cohorts at risk of disease and therefore, a critical need for the development and implementation of structured data collection tools to systematically identify patients with MHD and refer them to appropriate support services. While aggregated anonymised data shows that among GMS patients, 21.3% were identified for the CDM programme, 10.7% for OCF, and 15.8% for PP, CDM participation is the highest, indicating strong engagement in CDM. However, further research is needed to determine how many patients enrolled in, opted into, and attended the programme. Qualitative interviews revealed that GPs and GPNs frequently encounter patients with MHD in their settings.
Participants strongly agreed on the significant association between CVD and SMIs such as bipolar disorder and schizophrenia. They highlighted they were not aware of standardised protocols or frameworks for cardiovascular assessment in this population. Common cardiovascular risk factors identified during interviews included smoking, obesity, antipsychotic medication use, and family history.
Our findings show that GPs and GPNs perceive CVD risk assessment in patients with diagnosed MHD as opportunistic rather than systematic, underscoring the gap and highlighting the need for structured frameworks to support consistent practice.
Recommendations from participants included integrating mental health into CDM, expanding the GMS eligibility criteria, and delivering prevention initiatives through key workers such as practice-based coordinators or community mental health nurses and GPNs who maintain regular contact with patients and can support adherence to preventive care initiatives Reported barriers included limited resources, time constraints, and low patient motivation to engage in behavioural or therapeutic interventions. Furthermore, participants noted key research gaps, including the exclusion of patients with MHD from national chronic disease programmes, challenges in management of these patients within general practice, and a lack of focus on cardiovascular risk assessment in this population.
Comparison with existing literature
The prevalence of MHDs in general practice identified by the qualitative component of this study is high ranging between 20–50%. These rates are inconsistent with the prevalence rates reported by Ravichandran et al. as high as 56.3% pre-COVID, underscoring the lack of prevalence studies during or following COVID-19, necessitating the design of new studies focusing on mental health prevalence (Ravichandran et al. Reference Ravichandran, Dillon, McCombe, Sietins, Broughan, Connor, Gulati, Frawley, Kelly, B., Guérandel, Osborne and Cullen2025). The association between CVD and SMIs namely bipolar and schizophrenia observed in this study corresponds with the findings of Nielsen et al. (Nielsen et al. Reference Nielsen, Banner and Jensen2021). An Ireland-based study of adults over 50 years of age reported 80% increased risk of CVD and the risk increased with severity of depression (Fogarty et al. Reference Fogarty, McCombe, Brown, Van Amelsvoort, Clarke and Cullen2021).
While this study highlighted the similar approaches to CVD risk assessment in this population, namely Q-Risk estimation and the assessment of circulating biomarkers often conducted under national programmes, these practices contrast with the European Society of Cardiology (ESC) guidelines. The ESC recommends cardiovascular risk assessments for all adults, regardless of the presence of MHDs or known CVD risk factors (European Society of Cardiology 2025). Although QRISK calculators are commonly used to assess CVD risk, recent studies suggest that these tools may inadequately capture the elevated risk present in this population (Berry et al. Reference Berry, Drake, Webb, Ashcroft, Carr and Yung2018; Hippisley-Cox et al. Reference Hippisley-Cox, Coupland and Brindle2017). This underestimation is particularly concerning in younger populations, as individuals under 40 years of age with SMI may exhibit elevated CVD risk that remains undetected by standard QRISK-based guidelines, which are typically designed for older adults (Carolan et al. Reference Carolan, Hynes, McWilliams, Ryan, Strawbridge and Keating2023). Smoking and obesity emerged as the most common risk factors, consistent with the work of Compton et al. (Compton et al. Reference Compton, Daumit and Druss2006). The study highlights the need for integrated mental healthcare into general practice and developing structured frameworks. This corresponds with the work of Funk et al. which advocates for integration of mental health into primary healthcare systems (Funk et al. Reference Funk, Saraceno, Drew and Faydi2008).
Methodological challenges and limitations
The use of embedded mixed-methods design allowed us to combine aggregated practice-level data with qualitative insights from general practice professionals. Although the quantitative data were limited to aggregated indicators rather than individual-level patient records, they provided valuable contextual information regarding the availability of data collection tools and participation in CDM programmes. Integrating these findings with qualitative perspectives enhanced our understanding of how organisational structures and professional practices influence CVD risk assessment in patients with MHDs. To our knowledge, it is among the few studies exploring CVD assessment in patients with MHDs within general practice. A notable limitation of this study is the sample size. Recruiting general practices through university networks may have resulted in participants who are more engaged with research and quality improvement, introducing self-selection bias, which could limit the generalisability of our findings. Nevertheless, the study provides valuable insights into the feasibility of data collection, the use of CVD risk assessment tools, and the perspectives of general practice professionals, offering a foundation for future research in this area.
Implications for research, policy and practice
Based on the findings, cardiovascular assessment protocols for patients with MHD should be standardised and implemented nationally within general practice. This study supports the inclusion of individuals with certain mental health conditions in national CDM and PPs. Expansion of GMS criteria should also be considered, especially to include younger populations at risk, offering opportunities for early intervention.
Development of systematic electronic search tools like a “Mental Health Finder” and incorporation of such tools across all general practice software systems is essential for identifying patients with MHD for CVD screening. Such tools enable the capture of relevant patient data, facilitate the identification of those at risk and promote structured and consistent cardiovascular assessments within general practice. While the MHF tool was initially developed for the Socrates software, its expansion and standardisation across all general practice software systems are necessary to enable nationwide implementation. This will ensure consistent identification and flagging of at-risk patients. Integrating AI into these tools can further enhance their effectiveness by improving accuracy, streamlining triage, and supporting clinical decision-making in mental health care. Moreover, integration between primary and secondary care must be strengthened through clear referral pathways and coordinated care plans to ensure continuum of care in the patients. Interventions aimed at addressing modifiable CVD risk factors in this population should also be developed and prioritised. Future work might investigate association between clinical level demographics and CVD risk assessment in this population.
Conclusion
This mixed-methods study highlights the concerns regarding CVD risk among patients with MHDs seen in general practice. There is a clear need for structured frameworks and national support systems to ensure consistent cardiovascular assessment and management for this vulnerable population.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/ipm.2025.10158.
Acknowledgements
We would like to acknowledge support from University College Dublin’s School of Medicine, including its Clinical Research Centre and the College of Health and Agricultural Sciences. We would also like to thank the Health Services Executive, Ireland East Hospital Group, the Health Research Board and the UCD School of Medicine COINTREAU scheme for providing financial support.
Funding statement
This research received financial support from the COINTREAU scheme of the University College Dublin’s School of Medicine.
Data availability
Although participants’ identities are anonymised, the nature of the qualitative data poses a high risk of confidentiality breaches. This study’s dataset (i.e. interview transcripts) contains information that has a high likelihood of breaching study participants’ confidentiality if shared and therefore cannot be distributed publicly. The topic guide Appendix 1 and SRQR checklist were uploaded as supplementary files (S1 & S2) and are also available upon request.
Competing interests
No competing interests were disclosed.
Ethical standards
Ethical approval was granted by the UCD Human Research Ethics Committee on (LS-25-17-Broughan-Cullen), and the study is compliant with the Declaration of Helsinki. Written informed consent was obtained from the participants (GPs and GPNs) who agreed to participate in the interview.
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