Despite its relevance, most existing research on professional quality of life among audiologists has been conducted in high-income or developed country contexts, with comparatively little evidence available from low- and middle-income settings. In developed countries, clinical experiences have also been found to play a significant role in the professional quality of life of audiologists, with early-career audiologists exhibiting higher emotional exhaustion (Blood et al., 2008), and higher burnout and depersonalisation (Zimmer et al., 2022). This is particularly evident among audiologists working in public health sectors, serving the paediatric population (Severn et al., 2012). While these studies have provided important insight into the audiologists’ professional quality of life, the reality in low- and middle-income countries (LMICs) may be different, exacerbated by multiple factors such as systemic constraints including resource scarcity, high clinical workloads, limited infrastructure and reduced organisational support, alongside broader socioeconomic stressors common in LMIC health systems. (De Andrade et al., 2024; Masson, 2016; Nagdee & De Andrade, 2023).

This study is guided by resilience theory, which provides a framework for understanding how individuals adapt to stress and adversity (Van Breda, 2018). Resilience is conceptualised not as a fixed trait but as a dynamic and contextually shaped process influenced by personal, relational and environmental factors that support positive adaptation (Van Breda & Theron, 2018). This lens is particularly useful for examining how audiologists navigate occupational stressors, including emotional exhaustion, workload pressures and secondary exposure to trauma.

It enables the identification of protective factors that promote well-being and support sustainable professional functioning.

From a theoretical perspective, within the context of professional quality of life, resilience is conceptualised as a critical buffer against burnout and STS, while theoretically enhancing CS. Defined as the ability to thrive amidst adversity (Gonçalves et al., 2022), resilience encompasses psychological flexibility, self-efficacy and effective coping strategies (Campbell-Sills & Stein, 2007). Research shows that resilience is among the strongest predictors of CS (Pietrzak et al., 2014), and it is often linked to increased empathy and emotional strength derived from past adversity (Lim & DeSteno, 2016). Conceptually, understanding resilience as both a buffer and a source of growth allows for deeper insight into how audiologists maintain emotional balance and professional engagement in the face of ongoing challenges.

Van Breda (2018) further distinguishes resilience as both an outcome, recovering from adversity and a process, emphasising the mechanisms that enable positive adaptation over time. This distinction is particularly relevant in audiology, where professionals face both acute (e.g. traumatic patient encounters) and chronic (e.g. prolonged workload strain) stressors. In the South African context, resilience theory highlights culturally grounded coping strategies, community support and structural challenges, while interventions may focus on developing personal resources such as self-efficacy and self-compassion (Theron, 2016; Van Breda, 2018; Van Breda & Theron, 2018).

At a theoretical and policy level, interventions that aim to strengthen personal resources such as self-efficacy and self-compassion, particularly in resource-constrained settings, are conceptualised as having the potential to enhance professional quality of life (Gonçalves et al., 2022), making resilience a key target for policy development, professional training and workplace support.

The study aimed to examine levels of professional quality of life (CS, burnout and STS) among South African audiologists, to explore associations with resilience and coping, and to gain an in-depth understanding of how these experiences are perceived and managed in practice.

What is the professional quality of life of South African audiologists?

A two-phase, sequential explanatory mixed-methods design was used (Creswell & Creswell, 2018). In the first phase, a cross-sectional web-based survey was conducted to obtain quantitative data. Phase 1 employed a non-probability convenience sampling approach, whereby eligible South African audiologists voluntarily participated by responding to an online survey distributed via professional networks and social media platforms.

Quantitative data collected in phase 1 examined levels of professional quality of life, resilience and coping, as well as associations between these constructs and selected demographic and work-related variables. The second phase involved qualitative semi-structured interviews to further explore key quantitative findings. Phase 2 employed a non-probability purposive sampling approach, drawing on phase 1 participants who indicated willingness to participate in follow-up interviews and were selected to ensure variation in years of experience and work setting.

Specifically, phase 1 results showing patterns and associations between CS, CF, resilience, coping and demographic factors (such as age and professional experience) were used to identify areas requiring deeper exploration. These quantitative patterns directly informed the focus and content of the phase 2 interview guide, allowing the qualitative data to explain how and why these relationships were experienced in practice. Thus, the quantitative phase addressed ‘what’ patterns existed in professional quality of life, while the qualitative phase sought to explain ‘how’ and ‘why’ these patterns were experienced by audiologists.

The sample data for this research were collected from practising audiologists across diverse work settings in South Africa, including public hospitals, community health clinics, private practices, schools and academic institutions. Participants in phase 1 were recruited using non-probability convenience sampling to obtain information on professional quality of life among audiologists. The South African healthcare context is characterised by substantial variation in resource availability, workload demands and organisational support across work settings (Maphumulo & Bhengu, 2019; Mayosi & Benatar, 2014; Pillay et al., 2020). These contextual differences may shape experiences of CS, burnout and STS among audiologists (Khoza-Shangase & Mophosho, 2018; Statistics South Africa, 2023). Accordingly, the study sought to investigate whether professional quality of life differed across work settings. Phase 2 participants were purposively selected from the phase 1 sample to allow for in-depth exploration of experiences related to professional quality of life.

Participants were eligible to take part in the study if they were registered audiologists with the HPCSA, currently practising in any work setting in South Africa, and proficient in English, which was the language used for the survey and interviews. Audiologists who were not currently practising, were not HPCSA-registered, or who were unable to complete the survey in English were excluded.

Phase 1 used a non-probability convenience sampling approach to recruit South African audiologists registered with the HPCSA and practising in any setting within the country. Invitations were distributed via professional associations and social media platforms commonly used by audiologists.

An intended sample size of 283 participants was calculated based on the estimated population of registered audiologists in South Africa, using a 95% confidence interval, a 5% margin of error and a proportion of 50% (Olowofela & Isash, 2017). A total of 96 responses were initially received. Of these, 23 responses were excluded due to substantial missing or incomplete data, resulting in a final analysed sample of 23 participants.

Phase 2 employed non-probability purposive sampling. Participants were drawn from the phase 1 survey respondents who indicated willingness to be interviewed. Selection was guided by key quantitative patterns, particularly differences in CS, burnout, resilience, age and years of experience, to ensure inclusion of diverse perspectives.

An initial target of approximately 12–20 interviews was set, consistent with recommendations for thematic saturation in qualitative health research (Braun & Clarke, 2019). Recruitment continued until data saturation was reached, defined as the point at which no new themes were emerging, which occurred after 15 interviews.

Professional quality of life was measured using the Professional Quality of Life Scale, Version 5 (ProQOL v5), developed by Stamm (2010) to assess the positive and negative effects of caring work in helping professionals.

The phase 1 survey is provided in Online Appendix 1. Data were collected through a questionnaire that comprised informed consent information, demographic characteristics of participants and components of the ProQOL v5 scale, which consists of 3 sections and 30 items. The components of ProQOL v5 included CS, burnout and STS, with CF conceptualised as the combined impact of burnout and STS (Stamm, 2010). The components of ProQOL v5 were measured using a five-point Likert-type scale, ranging from 1 (never) to 5 (very often), with each subscale consisting of 10 items and possible scores ranging from 10 to 50. Higher scores reflect higher levels of the respective construct.

The ProQOL v5 was selected due to its widespread use in healthcare research and its established reliability and validity in assessing professional quality of life among helping professionals (Heritage et al., 2018; Sinclair et al., 2017; Stamm, 2010). The ProQOL v5 was particularly suited to this study because it captures both the positive (CS) and negative (burnout and STS) dimensions of caring work, allowing a balanced assessment of professional quality of life among audiologists working in emotionally demanding clinical contexts.

Resilience and coping were assessed using three self-report items included at the end of the survey. These items were designed to capture participants’ perceived ability to manage, recover from and adapt to work-related stress and challenges. The first item was an open-ended question: ‘How do you manage and recover from the stresses and challenges you encounter in your professional life, and how would you describe your ability to maintain your well-being despite these challenges?’ This item was analysed qualitatively. The second item assessed perceived resilience using a single Likert-type scale: ‘On a scale from 1 to 5, where one means “not at all resilient” and five means “extremely resilient,” how would you rate your resilience in managing professional stress and maintaining your well-being?’ Scores ranged from 1 to 5, with higher scores indicating greater perceived resilience. The third item assessed perceived coping using a single-item categorical scale: ‘Which of the following best describes your ability to cope with and recover from work-related stress and challenges?’ Responses ranged from ‘I often struggle to cope and feel overwhelmed’ to ‘I thrive under pressure and use challenges as opportunities for growth’, with higher categories reflecting stronger perceived coping. This item was treated as an ordinal measure of coping capacity. These single-item measures were included to minimise respondent burden in a longer survey and to allow participants to provide both quantitative and qualitative reflections on their resilience and coping.

However, unlike ProQOL, these items have not been formally psychometrically validated and should be interpreted as subjective indicators rather than standardised measures.

A semi-structured interview guide (Adeoye-Olatunde & Olenik, 2021) was developed to explain and expand on key findings from phase 1, in line with the explanatory sequential mixed-methods design. The phase 2 interview guide is provided as Online Appendix 2. The purpose of the interviews was to explore how participants experienced and interpreted patterns observed in the survey, particularly in relation to CS, CF, early-career challenges and self-reported resilience and coping. Interview questions were informed by the study aims and by phase 1 quantitative results, and were designed to elicit contextual explanations, examples and perceived drivers of professional quality of life. Quantitative findings related to resilience, coping and early-career experiences informed the development of interview questions.

A pilot study was conducted to test the feasibility of both phases (Irwin et al., 2008), involving two audiologists from different work settings. These participants completed the questionnaire and interviews and gave feedback via a structured form on clarity and relevance. They were excluded from the main study to avoid bias (Creswell, 2009).

The ProQOL scale is freely available for use without author’s permission, provided it is not sold, credited to the Centre for Victims of Torture (www.ProQOL.org), and unaltered except for translation or replacing the word ‘helper’ with a profession-specific term (Stamm, 2009). Permission to distribute the scale online was obtained from relevant professional associations, including South African Neurodevelopmental Therapy Association (SANDTA), South African Association of Audiologists (SAAA), National Black Association for Speech-Language and Hearing (NABSLHA), and Rural Rehab South Africa (RuReSA).

In phase 1, ProQOL v5 was administered via REDCap, a secure, user-friendly online platform, after signing a user agreement. The survey link included the research invitation, phase 1 information sheet and consent form.

Participants had 3 months to complete the questionnaire, with monthly reminders sent. At the end of the survey, participants could indicate a willingness to join phase 2 and provide contact details for follow-up. Phase 2 interviews were conducted via Microsoft (MS) Teams for convenience and cost-efficiency (De Villiers et al., 2021), enabling in-depth, semi-structured discussions (Ed. Cook, 2001; Knudsen et al., 2012). Each interview lasted 30 min – 45 min (Jamshed, 2014), was audio-recorded with participant consent (Carpenter & Suto, 2008) and securely stored (Frey & Beth, 2023). A distress protocol, adapted from Draucker et al. (2009), was prepared but not activated, as no participant showed signs of distress.

Where available, participants’ phase 1 survey responses (e.g. years of experience, work setting and professional quality of life scores) were reviewed prior to the interview to support probing and clarification during the qualitative discussion, while maintaining a flexible, participant-led interview approach.

Before data analysis was carried out, incomplete responses from the questionnaire were reviewed and excluded from the dataset. The Statistical Package for the Social Sciences (SPSS) version 29 (IBM Corporation, 2022) was used to analyse the quantitative data. Pearson’s correlation coefficients were used to determine the strength, direction and significance of relationships between the professional quality of life subscales, resilience and coping (Puth et al., 2014). Independent sample t tests were conducted to compare professional quality of life scores, coping strategies and resilience levels across different participants (Kim, 2019).

In the second phase, all interviews were audio-recorded with participant consent and transcribed verbatim by the researcher. Identifying information was removed during transcription, and participants were assigned participant numbers. Digital audio files and transcripts were stored on a password-protected computer and backed up on an encrypted external drive accessible only to the research team. Reflexive thematic analysis following Braun and Clarke’s (2006) six-step approach was used. Firstly, the data were familiarised through repeated reading of the transcripts, prior to coding. Secondly, initial codes were generated, guided by both the research questions and key quantitative findings (e.g. burnout, CS, resilience and early-career vulnerability). Thirdly, codes were organised into candidate themes that aligned with major quantitative patterns, such as CS, burnout and coping. Fourthly, these themes were reviewed and refined to ensure internal coherence and clear distinction between themes. Fifthly, themes were defined and named to reflect how they explained or expanded on the quantitative results. Finally, the report was produced using NVivo fourteen to organise and retrieve data segments, with participant quotes used to illustrate how qualitative findings contextualised and deepened understanding of the quantitative trends.

In accordance with the ProQOL v5 manual (Stamm, 2010), CF was not treated as a standalone subscale but was operationalised as a derived construct, calculated by combining scores from the burnout and STS subscales. This approach reflects the conceptualisation of CF as a higher-order construct encompassing both burnout and STS. Accordingly, analyses were conducted on individual professional quality of life subscales (CS, burnout and STS), as well as on the derived CF score, to allow for both nuanced and integrative interpretation of professional quality of life outcomes. Correlation analyses were conducted to examine associations between professional quality of life variables, resilience and coping. No formal adjustment for multiple comparisons was applied, and correlations were interpreted with caution, with emphasis placed on effect sizes and consistency with theoretical expectations.

Several strategies were used to enhance the credibility and trustworthiness of the qualitative analysis. An audit trail was maintained through reflective memos and coding notes documenting analytic decisions. Preliminary themes were reviewed and discussed with co-researchers to support peer debriefing and reduce individual bias. In addition, the integration of quantitative and qualitative findings allowed for triangulation, whereby themes were examined in relation to survey patterns. Together, these strategies strengthened the credibility, dependability and transparency of the qualitative findings.

This study was approved by the Human Research Ethics Committee (Non-Medical) of the University of the Witwatersrand, Johannesburg, South Africa (Clearance No. H24/09/38). All procedures followed the ethical principles outlined in the Declaration of Helsinki (World Medical Association, 2013), which emphasises the protection of participants’ rights, welfare and well-being. Before data collection, participants received detailed information about the study’s objectives, procedures, risks and benefits. Written informed consent was obtained from all participants. The study also followed data protection standards to ensure privacy and confidentiality throughout. Ethical approval and research design were reviewed to align with international ethical research standards (Emanuel et al., 2004; Jelsma & Clow, 2005).

The study assessed levels of CS and CF, comprising BO and STS, and examined their association with demographic variables in the sample.

The results revealed significant associations among the professional quality of life variables in the sample. A strong, significant, negative correlation was found between burnout and CS (r = −0.73; p < 0.001), indicating that higher burnout was linked to lower CS. Compassion fatigue also showed a moderate, significant, negative correlation with CS (r = −0.57; p < 0.001). An unexpected weak and non-significant negative correlation was observed between STS and CS (r = −0.22; p = 0.057). Secondary traumatic stress was moderately, significantly and positively correlated with burnout (r = 0.46; p < 0.001).

Correlation analyses examined relationships among ProQOL domains and self-reported resilience and coping (Table 3). Resilience showed a moderate, significant, positive correlation with CS (r = 0.43; p < 0.001) and moderate, significant, negative correlations with CF (r = −0.56; p < 0.001), burnout (r = −0.47; p < 0.001) and STS (r = −0.48; p < 0.001). In this study, resilience was quantitatively operationalised using a single self-report item included in the phase 1 survey, in which participants rated their perceived ability to manage professional stress on a five-point Likert scale (1 = not at all resilient to 5 = extremely resilient). This item captured subjective perceived resilience rather than a standardised psychometric construct and was therefore interpreted as an indicator of participants’ self-appraised coping capacity, rather than a validated resilience score. Similarly, coping was moderately, significantly and positively correlated with CS (r = 0.41; p < 0.001) but showed significant, negative correlations with CF (r = −0.54), burnout (r = −0.47; p < 0.001) and STS (r = −0.45; p < 0.001) (Table 3). These associations indicate that higher self-reported resilience and coping were linked to more favourable professional quality of life scores, although these relationships should be interpreted as correlational rather than causal.

Qualitative findings were then used to interpret these relationships by illustrating how participants experienced resilience, coping and emotional strain in their work.

The following section integrates quantitative associations with qualitative descriptions of resilience. Participants demonstrated moderate to high levels of resilience (M = 3.81, s.d. = 0.87), with slightly higher scores among older audiologists and those with more than 5 years of experience; however, these differences were not statistically significant across age, qualification, or experience groups (p > 0.05). Qualitative findings highlighted resilience as an important self-perceived capacity associated with more favourable professional quality of life experiences, shaped by emotional regulation, adaptability, problem-solving and strong collegial support. Qualitative findings were used to interpret the associations between self-reported resilience and professional quality of life observed in the survey. Audiologists described perseverance and composure in the face of stress, with a participant defining resilience as ‘the ability to go on and it’s the ability to bounce back’ (P25), and another noting, ‘You can’t get angry with the patient or … let them see that it’s affecting you’ (P30). Participants also demonstrated adaptability in navigating job uncertainty and resource limitations, as seen in P12’s and P49’s proactive problem-solving efforts. Support systems played a vital role, with several participants, such as P44 and P76, emphasising the importance of teamwork and shared resilience. These findings suggest that resilience supports audiologists’ professional well-being and enables them to continue providing care despite systemic and emotional challenges. These findings suggest that perceived resilience was linked to how audiologists experienced and managed their professional challenges, rather than implying a causal protective effect.

To further interpret the quantitative finding that resilience was positively associated with CS and negatively associated with burnout and CF, qualitative analysis revealed several self-reported strategies used by participants, including self-care practices, emotional regulation and professional boundary setting.

This section presents qualitative findings from phase 2 that were used to explain and expand on the quantitative associations observed in phase 1 between professional quality of life, resilience and coping. Interview data were analysed to identify the resources and strategies audiologists described as supporting or constraining their ability to manage stress, maintain CS and cope with burnout and STS.

This study has several limitations that should be considered when interpreting the findings. Firstly, although an intended quantitative sample size of 283 participants was calculated, the sample achieved comprised only 73 participants. This reduced sample size may have limited statistical power, particularly for subgroup comparisons across demographic variables and work settings and may have increased the risk of Type II errors in correlation analyses. As a result, it was not possible to conduct robust comparisons between private-sector and public-sector audiologists, despite the work setting being included as a study variable. Consequently, non-significant findings should be interpreted with caution, and future studies with larger and more evenly distributed samples are needed to examine organisational differences in professional quality of life more rigorously.

Secondly, the use of non-probability convenience sampling in phase 1 may have introduced self-selection bias, particularly as recruitment was conducted via professional associations, professional networks and social media platforms, which may have attracted audiologists with stronger views or experiences related to their professional quality of life.

Thirdly, the sample was predominantly female, younger and concentrated in private practice and public hospital settings. While this distribution reflects known trends within the South African audiology workforce, it may limit the generalisability of the findings to male audiologists, older practitioners and those working in underrepresented settings such as schools or rural community clinics.

Fourthly, although the HPCSA maintains registry data on registered audiologists, there is no single, comprehensive, publicly accessible dataset that provides detailed and directly comparable demographic information (e.g. age distribution, work setting and geographic location) for the audiology workforce at the time of the study. As a result, it was not possible to formally assess whether the achieved sample differed systematically from the broader audiologist population in South Africa.

Fifthly, while the ProQOL scale has demonstrated good psychometric properties internationally, there is limited published evidence regarding its psychometric performance specifically within South African audiologists. In addition, resilience and coping were assessed using single-item self-report indicators rather than standardised multi-item psychometric scales. While such items can capture global self-perceptions and reduce respondent burden, they do not allow for the same level of psychometric evaluation and may be more susceptible to measurement error. The absence of a comprehensive local psychometric evaluation may therefore affect the precision with which these constructs were measured in this sample.

Sixthly, given the number of correlation analyses conducted, the absence of formal adjustment for multiple testing increases the possibility that some smaller or marginally significant associations may be unstable or reflect Type I error. Findings should therefore be interpreted cautiously, particularly where effect sizes were small.

Finally, the cross-sectional design and reliance on self-report measures limit the ability to draw causal inferences and may be subject to response and recall bias. Despite these limitations, the mixed-methods design strengthened the study by allowing quantitative findings to be contextualised through rich qualitative data, enhancing the depth and credibility of the conclusions.