Understanding the training need priorities of the Australian Allied Health workforce: a national survey

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Yet, there is limited empirical insight into their current training priorities across core domains of professional practice. In an increasingly digital, team-based health system, identifying profession-specific and shared training needs is essential for designing development strategies that retain and grow a future-ready workforce. This study aimed to map training needs across the Australian AHP workforce and explore whether demographic factors predict perceived gaps between the importance and performance of key professional tasks, both within and across professional groups. Methods A national cross-sectional survey of AHPs was conducted using the WHO-endorsed Hennessy Hicks Training Needs Analysis Questionnaire. Participants self-rated 30 core and 10 emerging tasks by perceived importance and current performance. Gap scores (importance minus performance) were analysed and visualised using an Importance-Performance matrix to highlight priority areas. Descriptive statistics, multiple regression models and multivariate analysis explored whether variation existed between profession and other demographic variables across seven domains of practice. Results From 2,436 valid responses, a clear and consistent pattern emerged: AHPs rated all tasks as more important than their current performance, highlighting significant perceived capability gaps. The most pronounced needs were found in leadership/continuous improvement, and digital health domains. Emerging tasks such as managing work-life balance, achieving efficient results and identifying opportunities for quality improvement also ranked highly, reflecting the current landscape of AHP practice. While significant demographic differences including level of experience, highest qualification, profession and regionality were observed across domains, effect sizes were uniformly small, suggesting limited practical significance. Conclusion This study provides the first national snapshot of AHP needs and provides compelling evidence that the future of allied health workforce development lies in shifting from siloed, discipline-specific training to capability-building that supports interdisciplinary, digitally enabled healthcare. Interprofessional education models are particularly implicated to address shared training needs in leadership, digital transformation, and system improvement, particularly for early-career professionals and those in remote roles. These findings offer a practical and timely roadmap for aligning training with the evolving demands of healthcare delivery. Allied health training needs analysis capability development education inter-professional education leadership work-life balance Hennessey Hicks Training Needs Analysis Figures Figure 1 Figure 2 Figure 3 BACKGROUND Allied health professionals (AHPs) constitute a significant portion of the healthcare workforce, playing pivotal roles in delivering comprehensive, patient-centred care across various settings 1 , 2 . Despite their contributions, there is a paucity of assessments that address clinician training needs across various professional domains. Understanding these needs is essential for maintaining pace with the evolving healthcare landscape, marked by technological advancements and a shift towards collaborative, expanded scope care models. Emerging areas such as digital health, interprofessional practice, leadership, and quality improvement have become integral to effective practice. Identifying and addressing training needs within the allied health workforce is essential to ensure that professionals are equipped to meet current and future healthcare challenges. Training Needs Analysis (TNA) is a systematic process employed to identify gaps between current competencies and required skills within a workforce 3 . The literature is rich in describing the utility of TNA in various healthcare settings 4 , however evidence is lacking in this being applied across the Allied Health workforce. TNA serves as a foundational step in facilitating the identification of specific areas where training is most needed, enabling the development of tailored educational interventions that enhance service delivery and patient outcomes 5 . A literature review by Gould et al. emphasized that TNA is instrumental in informing the design of continuing education programs, ensuring that they are aligned with the actual needs of practitioners 3 . Moreover, TNA has been linked to improved job satisfaction and retention among AHPs, as it fosters a culture of continuous learning and professional growth 6 . Conducting training needs assessments is critical in healthcare for several reasons. Firstly, it ensures that educational resources are allocated efficiently, focusing on areas that will yield the most significant impact on patient care. Secondly, it promotes the development of capabilities that are responsive to evolving healthcare demands, such as the integration of digital technologies and interprofessional collaboration. The Centers for Disease Control and Prevention advocate for regular training needs assessments to maintain a competent public health workforce 7 . Similarly, the European Centre for Disease Prevention and Control emphasises the role of TNA in strengthening healthcare systems by identifying capacity gaps and informing workforce development strategies 8 . In the Australian context, the importance of TNA is underscored by the need to address predicted workforce shortages and ensure equitable access to quality care, particularly in rural and remote areas. A report by the Australian Government highlighted the necessity of targeted training initiatives to build a resilient and adaptable health workforce capable of meeting diverse community needs 9 . Recent studies have identified several emerging training priorities within the allied health sector 10 . Digital health competencies have become increasingly important, with AHPs expected to navigate electronic health records, telehealth platforms, and data analytic tools 11 . Leadership and management skills are also in demand, as AHPs often take on supervisory roles and migrate into roles that contribute to organisational decision-making 12 with very little training involved. Furthermore, interprofessional education (IPE) has gained prominence, promoting collaborative practice among healthcare professionals to enhance patient outcomes 13 . Interprofessional education (IPE) has been recognised as a strategy to address shared training needs among healthcare professionals. By fostering collaborative learning environments, IPE aims to improve communication, teamwork, and understanding of professional roles, ultimately enhancing patient care 14 . The World Health Organisation's Framework for Action on Interprofessional Education and Collaborative Practice advocates for the integration of IPE into health professional curricula, emphasising its role in improving healthcare delivery 15 . Implementing IPE requires a clear understanding of shared training needs across professions, which can be effectively identified through comprehensive TNA processes. The aim of the study was to undertake a training needs analysis of AHP’s working within the Australian public health sector and to identify similarities and differences in training needs across professions, as well as by years of experience, age, qualification, work setting and geographical region. This research seeks to determine whether demographic factors meaningfully influence workplace training needs, to better inform the design of targeted, evidence-based development programs that strengthen allied health professional capabilities and ultimately enhance the quality and efficiency of healthcare delivery. METHODS Design, setting and participants This national study used an analytical cross-sectional design conducted between May and August 2024. An online, self-administered questionnaire was distributed via email across participating public sector health districts. All AHP’s as defined by Allied Health Professions Australia 16 were considered eligible, whilst allied health assistants, health promotion officers, pathologists and welfare officers were excluded as their training needs were considered different. All participants in the study provided informed consent, indicating they understood the survey was anonymous and voluntary in nature prior to completion. Only participants who completed the survey in full were included in the analysis. Data collection Training needs of AHP were measured using the World Health Organisation–adopted Hennessy Hicks Training Needs Analysis Questionnaire (HHTNA-Q), a self-reported, close-ended structured questionnaire 17 . The HHTNA-Q was chosen for its psychometric testing for validity and reliability and its use previously in the healthcare sector 17 . The instrument is semi-opaque, meaning respondents were less likely to be able to distort their response and obtained data should accurately reflect their training requirements 4 . The questionnaire includes a set of 30 tasks that are relevant to the role of AHP’s, categorised into five superordinate domains: research/audit, communication/teamwork, clinical tasks, administration and management/ supervisory tasks. Respondents rate each of these tasks along a 7-point Likert scale according to two criteria: how important the task is to their job, and how well they feel they are currently performing the task. The greater the difference in the two ratings (importance minus performance) indicates the greater the training need for that task. There exists an allowance in the questionnaire design for an additional 10 items without invalidating the tool’s psychometric properties 18 . An additional 10 questions were developed from thematic reviews of the literature and previous local training needs data to ensure they had some validity, and described tasks considered contemporary and relevant to AHP’s. Subsequently, these questions were categorised into the domains ‘utilisation of digital health’ and ‘leadership / continuous improvement’, which reflected core training priorities of health professionals reported in literature from the last five years 19 – 21 . Scale reliability analysis was completed to confirm the 10 questions added did not impact the reliability of the tool. Statistical analysis Descriptive statistics were used to summarise importance and performance scores across the 40 tasks. Training needs were initially explored by calculating mean gap scores (importance minus performance) for each task and visualised using an importance-performance matrix. Paired-samples t tests were used to assess whether importance ratings significantly exceeded performance ratings and effect sizes were calculated using Cohen’s d to assess the magnitude of discrepancy between the two ratings. To explore the predictability of demographic variables, multiple linear regression models were constructed using mean performance score as the dependent variable. Each model included demographic variables (profession, age group, years of experience, highest qualification, work setting and work region). Multivariate analyses of variance (MANOVA) were conducted across all seven domains to test for differences in training need ratings using the same demographic factors. All analyses were conducted using IBM SPSS Statistics (Version 29) with statistical significance was set at p < 0.05. RESULTS A total of 2436 AHPs completed the survey from across five Australian states and one territory. The majority of participants were female (83%), between the ages of 28–43 (58%), had 11 years or more profession-based experience (58%), were working in a metropolitan region (76%) and a hospital setting (78%). A full demographic profile of participants is provided in Table 1 . [Insert Table 1 here] Scale reliability analysis demonstrated strong internal consistency for both the original 30-item HHTNA-Q (α = 0.93) and the modified 40-item questionnaire (α = 0.94). Paired-sample t-tests using the mean importance and performance task scores showed all comparisons were statistically significant (p < 0.001) and indicated consistency in the direction (performance < importance) and magnitude of the differences observed. This was further supported by most tasks scoring a positive Cohens d value, with effect size ranging from 0.06 (T29 – Undertaking administrative activities) to 1.12 (T36 - Managing a healthy work-life balance) suggesting a diverse range of training need priorities across the 40 tasks (Fig. 1 ). [Insert Fig. 1 here] For better visuality of the results, an Importance-Performance matrix was developed (Fig. 2 ), with the combined mean importance (y-axis = 5.74) and performance (x-axis = 5.17) scores across all 40 tasks serving as the thresholds for the quadrant axes. The distribution of tasks revealed 15 items fell within quadrant one (low importance/low performance), four items within quadrant two (high importance/low performance), 20 items within quadrant three (high importance/high performance), and one task was in quadrant four (low importance/high performance). The four tasks identified as high importance/low performance were: T12 – Accessing relevant literature for your clinical work, T36 – Managing a healthy work-life balance, T37 – Developing more effective ways to get results, and T38 – Identifying an opportunity for quality improvement. To examine whether demographic characteristics predicted perceived training need priority, separate linear regression models were conducted for each task in the high importance/low performance quadrant, using the mean score difference between importance and performance as the dependent variable. The initial model included age, gender, First Nations status, disability status, highest qualification, profession, years of experience, work setting and work region (based on the Modified Monash Model - MMM classification). Preliminary analyses indicated that gender, disability status, and First Nations status did not contribute significantly to any model and were excluded to reduce overfitting and improve model parsimony. All four task models were statistically significant at the p < 0.001 level, indicating that the included demographic variables collectively explained a significant proportion of the variance in training need scores (Table 2 ). The strongest model was for Task T37 (F = 26.01, p < 0.001, adjusted R² of 0.049). This model identified age, highest qualification, work region, and profession as significant predictors of perceived training gaps. Task T12 also demonstrated a meaningful model fit, (F = 21.20, p < 0.001, adjusted R² = 0.040), and showed significant influence from age, highest qualification, and work region. For Task T36, the model explained 3.9% of variance (adjusted R² = 0.037), with significant predictors including age and work region (F = 19.88, p < 0.001). Task T38 yielded similar findings, (F = 20.78, p < 0.001, adjusted R² = 0.039), with highest qualification, years of experience, and work region emerging as significant predictors. Across all four tasks, work region (MMM classification) was a consistent and significant demographic factor associated with perceived training need gaps, indicating variation in priorities based on rurality or remoteness. Age and highest qualification were also frequent predictors, highlighting their relevance in shaping training perceptions. Effect sizes were modest at best across all 4 high importance/low performance tasks (adjusted R² = 0.037–0.049), suggesting targeted training interventions may benefit from demographic tailoring, particularly across regional and remote geographic settings. A series of multivariate analyses of variance (MANOVAs) were conducted to examine the influence of demographic characteristics including age group, profession, years of experience, highest qualification level, primary work location, and geographical work region on training needs across seven professional practice domains. Training needs were calculated using mean importance–performance gap scores for each domain, with higher scores indicating greater perceived need. Initial two-way MANOVAs were performed to assess potential interaction effects between key demographic variables (e.g. age × experience); however, no statistically significant interaction terms were identified (p > 0.05 for all models). Accordingly, separate one-way MANOVAs were conducted for each demographic factor. Statistically significant multivariate effects were observed across all demographic variables examined. Specifically, significant effects were found for age group (F(21, 6978) = 6.455, partial η² = 0.018); years of experience (F(21, 6978) = 10.566, partial η² = 0.030); profession (F(126, 15857) = 2.296, partial η² = 0.017); qualification level (F(42, 11387) = 4.235, partial η² = 0.012); work setting (F(14, 4422) = 3.770, partial η² = 0.012); and work region (F(42, 11387) = 4.602, partial η² = 0.013). While all multivariate effects reached statistical significance (all < 0.001), effect sizes were generally small, suggesting modest differences in training needs between demographic subgroups. The strongest observed effect was for years of experience. Follow-up univariate ANOVAs were conducted to explore domain-level effects in more detail. These analyses demonstrated that demographic factors influenced perceived training needs across all seven domains to varying degrees (Table 3 ). Highest qualification level and years of experience emerged as the most consistent predictors across domains, particularly in the research/audit and communication/teamwork domains. The largest individual effect sizes were observed for work region in the leadership/continuous improvement domain (partial η² = 0.048) and the management/supervisory domain (partial η² = 0.036). In contrast, administrative tasks and digital health utilisation domains showed relatively minimal variation by demographic subgroup, indicating that training needs in these areas may be more universally experienced across the allied health workforce. Post hoc comparisons were performed using Tukey’s Honestly Significant Difference test where omnibus ANOVA results indicated statistically significant group differences. In the research and audit domain, significant differences were observed across age, qualification, profession, work setting, and work region. Younger professionals aged 18–27 years reported significantly higher training needs than those aged 44 years and older. Respondents with diploma- and bachelor-level qualifications reported greater needs than those with doctoral qualifications. Several professional groups (Groups 2, 4–10, 13–14) reported higher training needs than Profession Group 25, although Group 25 consistently reported lower scores across all domains (Fig. 3 ). This pattern suggests that many of the observed post hoc differences may be attributed to the overall low self-assessments reported by Group 25, rather than notably elevated needs among other professions. Higher training needs were also found among those working in hospital, community, or primary care settings, compared to those in addiction and mental health services. Professionals located in very remote areas (MMM 7) reported higher needs than those in metropolitan (MMM 1) and regional (MMM 4) areas. In the communication and teamwork domain, younger respondents again reported higher needs than all other age groups. Interestingly, those aged 28–43 years rated their needs lower than those aged 44 and older. Experience showed a similar pattern, with professionals who had five or fewer years of experience reporting greater needs than those with more than six years. While differences across qualification levels were modest, doctoral-qualified professionals again reported the lowest needs. Regionally, respondents in MMM 4 reported greater training needs than those in MMM 1, whereas MMM 7 reported lower needs than MMM 2. For clinical tasks, younger and less experienced AHPs again reported significantly greater training needs. Bachelor-qualified professionals reported higher needs than doctoral-qualified peers. Regional differences were also observed, with MMM 3 reporting greater needs than MMM 7. Profession-level comparisons were again influenced by the lower mean scores observed in Group 25. In the administrative tasks domain, no statistically significant differences were detected between professional groups during post hoc testing. In the management and supervisory tasks domain, middle-aged AHPs (28–59 years) reported greater training needs than the youngest age group (18–27 years). Qualification level again differentiated need, with bachelor-level respondents reporting higher needs than those with doctoral-level education. Regionally, those working in MMM 2 reported higher needs than those in MMM 6 and MMM 7, indicating potentially reduced emphasis on supervisory development in more remote areas. In the digital health utilisation domain, professionals with honours or master’s degrees reported greater training needs than doctoral-qualified peers. Regionally, MMM 3 reported greater needs than MMM 7, and professionals working in addiction and mental health services reported higher needs than those in hospital, primary care, or community settings. In the leadership and continuous improvement domain, respondents from MMM 7 locations reported significantly higher needs than those from all other regional classifications. Once again, younger age and lower levels of experience were associated with higher training needs. Several professional groups reported higher needs than Group 25, consistent with earlier domain comparisons. Although many demographic variables demonstrated statistically significant effects, overall effect sizes were small. The pattern of results suggests that perceived training needs are not strongly or consistently predicted by demographic characteristics. Notably, several significant findings appeared to be driven by a small number of outlier groups; particularly Profession Group 25, which consistently reported lower scores across all domains. These findings suggest that while demographic factors such as age, profession, qualification, experience, work setting, and region may influence training needs to a degree, universal training strategies may be more appropriate than narrowly targeted interventions in most cases. DISCUSSION This study sought to identify the priority training needs of Australian AHPs working in the public sector and explored whether these needs differed by demographic group or were shared across professions. Prior to this research, there was limited cohesive evidence identifying workforce-wide capability gaps in AHPs or to what extent training needs clustered within or between professional groups. Our findings revealed that demographic characteristics such as profession, age, years of experience, work setting, and work region only explained a small proportion of the variance in training needs. Regression models returned adjusted R-squared values below 0.06, even for tasks with the largest importance-performance gaps. This suggests that while some demographic variables had statistically significant effects; particularly profession and experience, the overall impact was modest at best. Similar observations were observed at the training domain level, indicating limited predictive value of demographic variables for training need differences. These results indicate that workforce development strategies should consider, but not focus on demographic segmentation and instead adopt a task- and domain-based approach, based on identified priority needs. Early-career professionals and those in remote areas may benefit from targeted access and support strategies, but training design should be guided primarily by the competencies required across professional domains, ensuring that educational offerings align with common role expectations and emerging healthcare priorities. Further, other factors not captured in this study, such as organisational culture, access to supervision, leadership opportunities, and time pressures, may have a greater influence 22 , 23 and should also be carefully considered in workforce training and development initiatives. A consistent pattern across all 40 tasks was that importance ratings exceeded performance ratings. This discrepancy highlights a perceived gap between what AHPs believe to be essential for effective practice and their current confidence or capability in performing those tasks. Such gaps may reflect limited access to training, supervision, or opportunities to apply skills in the workplace. Prioritising these identified needs through targeted training can enhance both skill development and confidence, which has been shown to improve job satisfaction and workforce engagement 24 . Moreover, research suggests that planned and responsive professional development initiatives contribute positively to workforce retention, particularly when staff perceive that their individual development needs are acknowledged and addressed 24 – 26 . Of particular interest, three of the ten additional questions included in the survey emerged as high-priority training needs: managing a heathy work-life balance, identifying an opportunity for quality improvement and developing more effective ways to get results. These items reflect emerging areas of practice and broader shifts in healthcare delivery, suggesting that contemporary training needs are evolving beyond traditional clinical competencies. Work-life balance has become increasingly critical in the context of rising burnout and workload intensification in healthcare settings 27 , 28 . Similarly, value-based healthcare is gaining momentum as systems strive to align outcomes with cost-effectiveness and patient-centred care, necessitating new capabilities in economic and strategic thinking 29 . The use of digital enabled technology, such as artificial intelligence, in clinical decision-making and workflow optimisation is also accelerating, requiring health professionals to develop digital literacy and ethical reasoning skills 30 , 31 . These findings suggest that contemporary training programs must be agile and future-oriented, equipping AHPs with skills to navigate technological, systemic, and personal challenges in a transforming healthcare landscape. Notably, the largest average training gaps were observed in the domains of Leadership and Continuous Improvement and Utilisation of Digital Health. These findings are aligned with broader workforce development literature, which underscores the growing need for system-level capabilities in areas such as digital transformation, strategic leadership, and quality improvement 32 , 33 . As healthcare becomes more complex and team based, AHPs must be equipped with skills to lead change, integrate technology, and drive service innovation. These insights are reinforced by evidence suggesting that AHPs who engage in reflective practice and continuous learning contribute to more efficient, effective, and adaptive health services 34 – 36 . As such, training initiatives should be designed to develop innovation literacy, design thinking, and improvement science methodologies. Leadership training emerged as a consistently high priority need across all professional groups. This supports existing literature showing that AHPs are increasingly expected to take on leadership roles without the benefit of formal training, creating challenges in team management and strategic execution 37 , 38 . Targeted leadership development programs can support AHPs to fulfil these expectations, benefiting both patient care and workforce sustainability. Another critical finding was the consistent prioritisation of work-life balance training across all demographic groups and professions. This aligns with substantial evidence on the negative impact of stress, burnout, and workload on healthcare professionals’ well-being and service quality 27 , 28 . The high rating of this item underscores the importance of training that supports psychological resilience, stress management, and sustainable work practices. Importantly, the relatively small variation in training needs by profession provides a strong argument for leveraging interprofessional education (IPE) models. IPE, which enables professionals to learn with, from, and about one another, has been shown to improve communication, collaboration, and patient outcomes 14 , 39 . Given the consistency of priority training needs across domains and disciplines in this study, IPE offers an efficient and effective approach to training design and delivery. It also ensures consistency in capability development and fosters shared understanding among healthcare teams 40 . Limitations While this study contributes novel insights into AHP training needs, several limitations should be acknowledged. The cross-sectional, self-reported nature of the data may introduce bias, and although the sample was large and representative of the Australian public health sector workforce, it may not capture the full diversity of experiences across settings. Additionally, training needs are dynamic and context-dependent; thus, several authors have reported that a single survey instrument cannot fully capture the complexity of learning and development requirements 8 , 41 , 42 . Future research should incorporate mixed methods approaches, including qualitative interviews and longitudinal studies, to better understand how training needs evolve and how interventions impact capability over time. CONCLUSION This training needs analysis offers a timely and comprehensive examination of the evolving development priorities for Australia’s allied health workforce. The findings highlight a clear need to modernise professional development approaches; shifting from traditional, discipline-specific models towards capability-building that reflects the realities of interdisciplinary, digitally enabled, and rapidly transforming healthcare environments. While multivariate analyses revealed statistically significant associations between demographic factors and domain-specific training needs; effect sizes were uniformly small, with no consistent trends observed in post hoc comparisons, suggesting limited practical significance. Some variables such as experience, age and qualification level showed some predictive value, particularly for early-career professionals and those in generalist or remote roles. However, training needs were not systematically elevated across these groups, and no demographic variable emerged as a strong predictor of training demand. Instead, shared challenges were identified across professions, work locations and regions, particularly in the domains of leadership and continuous improvement and digital health literacy. These areas reflect contemporary system-wide shifts in healthcare priorities, including the need for adaptive leadership, data-informed practice, and cross-disciplinary collaboration 32 . The consistent gap between the perceived importance and self-rated performance across tasks underscores an urgent need for responsive, targeted training. Interprofessional education (IPE) models are especially well-suited to address these shared learning needs in high-priority domains and offer efficiency, consistency, and collaborative benefits. However, training strategies should retain sufficient flexibility to accommodate specific needs driven by context or demographic factors. Taken together, these insights provide an actionable roadmap for allied health workforce development that is both evidence-informed and aligned with the broader goals of healthcare reform. Investing in targeted, domain specific, and scalable training across all sectors and experience levels, will not only enhance allied health workforce capability, but also contribute to professional satisfaction, service quality, and system sustainability. Abbreviations AHP Allied Health Professionals HHTNA Q–Hennessy Hicks Training Needs Analysis Questionnaire IPE Inter–professional education MANOVA Multivariate analysis of variance MMM Modified Monash Model TNA Training Needs Analysis Declarations Ethics approval and consent to participate Ethical approval for this study was granted by the Metro South Human Research Ethics Committee (HREC/2024/QMS/105880). The study was conducted in accordance with the principles of the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines. Informed consent was obtained from all participants prior to their participation in the study. Clinical trial number Clinical trial number not applicable Consent for publication Not applicable. Availability of data and materials The dataset supporting the conclusions of this article is included within the article (and its additional files). Competing interests The authors declare that they have no competing interests. Funding The authors declare that no financial support was received. Authors’ contributions JB conceptualised the study, developed the research protocol, coordinated ethics and governance approvals, led participant recruitment, conducted data collection, performed statistical analyses, and drafted the manuscript. KA contributed to protocol development, coordinated recruitment activities, assisted with data preparation, and contributed to manuscript revision. CL contributed to the development of the protocol, supported data interpretation, and critically reviewed the manuscript for intellectual content. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. The study manuscript adheres to the International Committee of Medical Journal Editors (ICJME) (http://www.icmje.org) definition of authorship. All authors read and approved the final manuscript. Acknowledgements We gratefully acknowledge the allied health clinicians and health services who participated in this study across Australia. References Lizarondo L, Turnbull C, Kroon T, Grimmer K, Bell A, Kumar S, et al. Allied health: integral to transforming health. Aust Health Rev. 2016;40(2):194–204. Walker M, Stanton P, Halvorsen B, Cavanagh J, Bartram T. Allied health professionals: hidden but essential. Research handbook on contemporary human resource management for health care. Edward Elgar Publishing; 2024. pp. 149–63. Gould D, Kelly D, White I, Chidgey J. Training needs analysis. A literature review and reappraisal. Int J Nurs Stud. 2004;41(5):471–86. Markaki A, Malhotra S, Billings R, Theus L. Training needs assessment: tool utilization and global impact. BMC Med Educ. 2021;21(1):310. Holloway K, Arcus K, Orsborn G. Training needs analysis - The essential first step for continuing professional development design. Nurse Educ Pract. 2018;28:7–12. Shiri R, El-Metwally A, Sallinen M, Poyry M, Harma M, Toppinen-Tanner S. The Role of Continuing Professional Training or Development in Maintaining Current Employment: A Systematic Review. Healthc (Basel). 2023;11:21. O'Meara MG, Sobelson RK, Trigoso SM, Kramer RE, McNaughton C, Smartis RJ, et al. Ensuring a competent public health responder workforce: The CDC experience. J Emerg Manag. 2019;17(3):199–209. Czabanowska K, Rodriguez Feria P. Training needs assessment tools for the public health workforce at an institutional and individual level: a review. Eur J Public Health. 2024;34(1):59–68. Kolt G. Development of and support for the rural and remote allied health workforce in Australia Australian Council of Deans of Health Sciences Limited; 2025 22 March 2025. Dalton LM, Hills AP, Jayasinghe S, Strong K, Hyland P, Byrne NM. The Allied Health Expansion Program: Rethinking how to prepare a workforce to enable improved public health outcomes. Front Public Health. 2023;11:1119726. Longhini J, Rossettini G, Palese A. Digital Health Competencies Among Health Care Professionals: Systematic Review. J Med Internet Res. 2022;24(8):e36414. Bradd P, Travaglia J, Hayen A. Leadership in allied health: A review of the literature. Asia Pac J Health Manage. 2017;12(1):17–24. Mahajan R, Mohammed CA, Sharma M, Gupta P, Singh T. Interprofessional Education: An Approach to Improve Healthcare Outcomes. Indian Pediatr. 2018;55(3):241–9. Oudbier J, Verheijck E, van Diermen D, Tams J, Bramer J, Spaai G. Enhancing the effectiveness of interprofessional education in health science education: a state-of-the-art review. BMC Med Educ. 2024;24(1):1492. Gilbert JH, Yan J, Hoffman SJ. A WHO report: framework for action on interprofessional education and collaborative practice. J Allied Health. 2010;39(Suppl 1):196–7. Allied Health Professions Australia. About AHPA n.d [Available from: https://www.ahpa.com.au/about-ahpa/ Hicks C, Hennessy D. The use of a customized training needs analysis tool for nurse practitioner development. J Adv Nurs. 1997;26(2):389–98. Hennessy D, Hicks C. Hennessy-Hicks training needs analysis (TNA) questionnaire and manual2011. Available from: http://epapers.bham.ac.uk/3453/ Brennan K, Woodley L, Winstead C. Bridging the gap: Innovating undergraduate public health nursing education through a service-learning health fair project. Public Health Nurs. 2024;41(4):825–8. Milgate W, Copley J, Hill J. Failing professional practice placements in allied health: What do we understand about the student experience? A scoping review. Adv Health Sci Educ. 2024;29(1):301–27. Jarva E, Oikarinen A, Andersson J, Pramila-Savukoski S, Hammarén M, Mikkonen K. Healthcare professionals' digital health competence profiles and associated factors: A cross‐sectional study. J Adv Nurs. 2024;80(8):3236–52. Schwartz R, Rollins A, Grant A. Capability building in multidisciplinary teams: the role of supervision and reflective practice. Int J Health Plann Manage. 2019;34(3):e1252–63. Rollins A, Schwartz R, Lee A. Organisational culture and professional development in healthcare: a systematic review. J Health Organ Manag. 2021;35(5):619–36. n den Berg TI, Elders LA, de Zwart BC, Burdorf A. The effects of work-related and individual factors on the Work Ability Index: a systematic review. Occup Environ Med. 2009;66(4):211–20. Halter M, Pelone F, Boiko O, Beighton C, Harris R, Gale J, et al. Interventions to Reduce Adult Nursing Turnover: A Systematic Review of Systematic Reviews. Open Nurs J. 2017;11:108–23. Kyndt E, Gijbels D, Grosemans I, Donche V. Teachers’ everyday professional development: a review of the literature. Teacher Teacher Educ. 2016;55:23–33. Johnson J, Louch G, Dunning A, Johnson O, Grange A, Reynolds C, et al. Burnout mediates the association between depression and patient safety perceptions: A cross-sectional study in hospital nurses. J Adv Nurs. 2017;73(7):1667–80. Askari R, Rafiei S, Akbari R, Ebrahimi EH, Dehghani A, Shafii M. The relationship between work-life balance and quality of life among hospital employees. Int J Healthc Manag. 2021;14(2):436–40. Porter ME, Lee TH. The strategy that will fix health care. Harv Bus Rev. 2013;91(10):50–70. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43. Topol E. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK; 2019. Frenk J, Chen L, Bhutta ZA, Cohen J, Crisp N, Evans T, et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet. 2010;376(9756):1923–58. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res. 2017;19(11):e367. Smith L, Jones K, Taylor M. Adapting to change: the importance of continuous improvement in allied health. BMJ Open. 2020;10(5):e037687. Jones S, Brown A. Sustaining evidence-based practice: a case for ongoing education. BMC Health Serv Res. 2019;19(1):720. Miller R, Clarke R. Innovation in allied health: harnessing design thinking for better service delivery. Aust Health Rev. 2020;44(6):776–81. McKeever M, Brown C. Leading from the middle: experiences of allied health professionals in leadership roles. J Allied Health. 2019;48(3):e67–373. Taylor M, Williams C, Jordan R. Preparing allied health professionals for leadership: a national scoping study. J Health Leadersh. 2021;13:45–55. Reeves S, Fletcher S, Barr H, Birch I, Boet S, Davies N, et al. A BEME systematic review of the effects of interprofessional education: BEME Guide 39. Med Teach. 2016;38(7):656–68. Rosen MA, DiazGranados D, Dietz AS, Benishek LE, Thompson D, Pronovost PJ, et al. Teamwork in healthcare: Key discoveries enabling safer, high-quality care. Am Psychol. 2018;73(4):433. Al-Ismail MS, Naseralallah LM, Hussain TA, Stewart D, Alkhiyami D, Abu Rasheed HM, et al. Learning needs assessments in continuing professional development: a scoping review. Med Teach. 2023;45(2):203–11. Pang M, Sayner A, McKenzie K. Continuing professional development training needs of allied health professionals in regional and rural Victoria. Aust J Rural Health. 2024;32(4):763–73. Tables Table 1 Total ( n =2436) n (%) Gender Male 310 (13) Female 2,019 (83) Transgender 61 (2.5) Non-binary 23 (0.9) Not disclosed 23 (0.9) Age 18-27 288 (12) 28-43 1,409 (58) 44-59 627 (26) 60-69 105 (4.3) 70-78 7 (0.3) Identifies as First Nations person Yes 33 (1.4) No 2,375 (97) Not disclosed 28 (1.1) Living with a disability Yes 50 (2.1) No 2,347 (96) Not disclosed 39 (1.6) Highest qualification Certificate/Diploma 20 (0.8) Bachelor 823 (34) Honours 616 (25) Masters 870 (36) Doctoral 107 (4.4) Years of experience <2 164 (6.7) 2 – 5 382 (16) 6 - 10 503 (21) 11+ 1,387 (57) Profession Physiotherapist 477 (19.6) Occupational Therapist 417 (17.1) Social Worker 413 (17.0) Dietitian/Nutritionist 259 (10.6) Speech Pathologist 228 (9.4) Psychologist 180 (7.4) Pharmacist 125 (5.1) Radiographer/Medical Imaging Technologist 91 (3.7) Podiatrist 77 (3.2) Orthoptist 28 (1.2) Exercise Physiologist 27 (1.1) Sonographer 27 (1.1) Audiologist 24 (1.0) Genetic Counsellor 14 (0.6) Medical/Radiation Therapist 14 (0.6) Orthotist/Prosthetist 11 (0.5) Music Therapist 10 (0.4) Art Therapist 8 (0.3) Leisure/Diversional Therapist 5 (0.2) Physicist 1 (0.0) State/Territory New South Wales 937 (38.5) Queensland 661 (27.1) Western Australia 383 (15.7) Victoria 234 (9.6) South Australia 147 (6.0) Northern Territory 74 (3.0) Australian Capital Territory 0 (-) Tasmania 0 (-) Primary work location Hospital 1,731 (71.2) Community 307 (12.6) Addiction and Mental Health 179 (7.3) Not listed 219 (9.0) Work region (based on Modified Monash Model) MM1 1,845 (75.7) MM2 56 (2.3) MM3 129 (5.3) MM4 83 (3.4) MM5 127 (5.2) MM6 8 (0.3) MM7 188 (7.7) [ Table 2 title ] Linear regression analysis of top four prioritised tasks Task F p-value R 2 Adjusted R 2 Demographic influence (p<0.05) T12 21.20 <0.001 0.042 0.040 Moderate; age, highest qualification, work region (MMM) T36 19.88 <0.001 0.039 0.037 Moderate; age, work region (MMM) T37 26.01 <0.001 0.051 0.049 Moderate; age, highest qualification, work region (MMM), profession T38 20.78 <0.001 0.041 0.039 Moderate; highest qualification, years of experience, work region (MMM) Table 2: Logistic regression results for high importance/low performance tasks (T12, T36, T37, T38), showing the influence of demographic factors on the individual tasks score differences (showing p<0.05). Predictors included in the final modelling were age, work setting, work region (MMM), years of experience, highest qualification, and profession. [ Table 3 title ] Univariate analysis by task domain (D1 – D7) Domain Variable F Partial η² P-value D1: Research/Audit Age group 4.030 0.005 0.007 Qualification level 17.692 0.042 <0.001 Profession 4.752 0.034 <0.001 Work location 16.773 0.015 <0.001 Work region 4.715 0.011 <0.001 D2: Communication/Teamwork Age group 20.788 0.025 <0.001 Qualification level 6.196 0.015 <0.001 Years of experience 29.476 0.035 <0.001 Work region 3.999 0.010 <0.001 D3: Clinical tasks Age group 23.347 0.028 <0.001 Qualification level 13.628 0.033 <0.001 Profession 3.044 0.022 <0.001 Years of experience 36.786 0.043 <0.001 Work location 4.876 0.004 0.008 Work region 6.441 0.016 <0.001 D4: Administration Profession 2.223 0.016 0.002 D5: Management/Supervisory Age group 13.313 0.016 <0.001 Qualification level 2.499 0.006 0.021 Profession 2.274 0.017 0.002 Years of experience 19.428 0.023 <0.001 Work location 3.173 0.003 0.042 Work region 15.340 0.036 <0.001 D6: Utilisation of digital health Qualification level 4.432 0.011 <0.001 Profession 3.222 0.023 <0.001 Work location 8.827 0.008 <0.001 Work region 5.041 0.012 <0.001 D7: Leadership/Continuous improvement Age group 8.901 0.011 <0.001 Qualification level 3.532 0.009 0.002 Profession 1.983 0.015 0.008 Years of experience 8.157 0.010 <0.001 Work location 10.261 0.009 <0.001 Work region 20.563 0.048 <0.001 [ Table 3 legend ] Significant effects of demographic variables on training needs across the seven domains (univariate analysis). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in BMC Medical Education → Version 1 posted Editorial decision: Revision requested 09 Sep, 2025 Reviews received at journal 08 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviews received at journal 20 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 19 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers invited by journal 13 Jul, 2025 Editor assigned by journal 13 Jul, 2025 Editor invited by journal 11 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 2025 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. 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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-7005080","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484828124,"identity":"c71970a2-4e3f-4308-bb2f-54b264b4c673","order_by":0,"name":"J Bartholomew","email":"data:image/png;base64,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","orcid":"","institution":"Logan \u0026 Beaudesert Hospital Service","correspondingAuthor":true,"prefix":"","firstName":"J","middleName":"","lastName":"Bartholomew","suffix":""},{"id":484828125,"identity":"a31abdea-c5cb-45e4-b652-afaf152faed4","order_by":1,"name":"K Adams","email":"","orcid":"","institution":"Logan \u0026 Beaudesert Hospital Service","correspondingAuthor":false,"prefix":"","firstName":"K","middleName":"","lastName":"Adams","suffix":""},{"id":484828126,"identity":"a4030882-2b7f-41d3-aa43-914d14ba4c38","order_by":2,"name":"C Louwen","email":"","orcid":"","institution":"Logan \u0026 Beaudesert Hospital Service","correspondingAuthor":false,"prefix":"","firstName":"C","middleName":"","lastName":"Louwen","suffix":""}],"badges":[],"createdAt":"2025-06-30 00:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7005080/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7005080/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12909-025-08141-3","type":"published","date":"2025-11-05T15:57:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87033499,"identity":"baad1206-9e1e-4938-bd4c-d16ee44458ee","added_by":"auto","created_at":"2025-07-18 13:06:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":621754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e[Figure 1 title] \u003c/em\u003eEffect sizes for Importance versus Performance ratings for each task\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eFigure 1 legend\u003c/em\u003e] \u003cstrong\u003eFigure 1:\u003c/strong\u003e Effect sizes (Cohen’s d) for differences between importance and performance ratings across 40 tasks, with task numbers shown.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7005080/v1/d74bf4082465efa723aae5bf.jpg"},{"id":87035572,"identity":"ddff94b0-dd8d-4eaf-aba9-bd1abeb841b6","added_by":"auto","created_at":"2025-07-18 13:14:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227821,"visible":true,"origin":"","legend":"\u003cp\u003e[\u003cem\u003eFigure 2 title\u003c/em\u003e] Importance – Performance Matrix (tasks by domain)\u003c/p\u003e\n\u003cp\u003eThe matrix plots average performance (x-axis) against average importance (y-axis) ratings for each of the 40 tasks included in the training needs assessment. Tasks are represented by unique markers indicating their associated domain (D1–D7), based on predefined groupings. The quadrant boundaries are defined by the overall mean performance (5.17) and importance (5.74) scores. Quadrants represent: high importance/low performance (‘priorities’), high importance/high performance, low importance/high performance, and low importance/low performance.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7005080/v1/b162cc680d468145dfcf60cc.jpg"},{"id":87033498,"identity":"e82746b9-c760-4731-a71d-5ba8af7a85e8","added_by":"auto","created_at":"2025-07-18 13:06:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":444991,"visible":true,"origin":"","legend":"\u003cp\u003e[\u003cem\u003eFigure 3 title\u003c/em\u003e] Mean training scores by profession and domain\u003c/p\u003e\n\u003cp\u003eHeatmap of mean training need scores (higher scores indicate greater need) across domains 1 – 7 by profession (n = 25). Profession 25 consistently reports lower training needs across all domains relative to other groups, influencing multiple significant results in post hoc comparisons. This visualisation underscores the variation in self-perceived training needs across disciplines, suggesting that profession-specific tailoring may be warranted in some areas.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7005080/v1/2f7f921900fc2d60e11b48a9.jpg"},{"id":95564036,"identity":"63030230-08af-4e00-84f0-f521dea0856f","added_by":"auto","created_at":"2025-11-10 16:06:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2048146,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7005080/v1/adc81966-0c11-4592-bbcb-bb583829bd8c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the training need priorities of the Australian Allied Health workforce: a national survey","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAllied health professionals (AHPs) constitute a significant portion of the healthcare workforce, playing pivotal roles in delivering comprehensive, patient-centred care across various settings\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite their contributions, there is a paucity of assessments that address clinician training needs across various professional domains. Understanding these needs is essential for maintaining pace with the evolving healthcare landscape, marked by technological advancements and a shift towards collaborative, expanded scope care models. Emerging areas such as digital health, interprofessional practice, leadership, and quality improvement have become integral to effective practice. Identifying and addressing training needs within the allied health workforce is essential to ensure that professionals are equipped to meet current and future healthcare challenges.\u003c/p\u003e\u003cp\u003eTraining Needs Analysis (TNA) is a systematic process employed to identify gaps between current competencies and required skills within a workforce \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The literature is rich in describing the utility of TNA in various healthcare settings\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, however evidence is lacking in this being applied across the Allied Health workforce. TNA serves as a foundational step in facilitating the identification of specific areas where training is most needed, enabling the development of tailored educational interventions that enhance service delivery and patient outcomes\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. A literature review by Gould et al. emphasized that TNA is instrumental in informing the design of continuing education programs, ensuring that they are aligned with the actual needs of practitioners\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Moreover, TNA has been linked to improved job satisfaction and retention among AHPs, as it fosters a culture of continuous learning and professional growth\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eConducting training needs assessments is critical in healthcare for several reasons. Firstly, it ensures that educational resources are allocated efficiently, focusing on areas that will yield the most significant impact on patient care. Secondly, it promotes the development of capabilities that are responsive to evolving healthcare demands, such as the integration of digital technologies and interprofessional collaboration. The Centers for Disease Control and Prevention advocate for regular training needs assessments to maintain a competent public health workforce\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Similarly, the European Centre for Disease Prevention and Control emphasises the role of TNA in strengthening healthcare systems by identifying capacity gaps and informing workforce development strategies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e In the Australian context, the importance of TNA is underscored by the need to address predicted workforce shortages and ensure equitable access to quality care, particularly in rural and remote areas. A report by the Australian Government highlighted the necessity of targeted training initiatives to build a resilient and adaptable health workforce capable of meeting diverse community needs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRecent studies have identified several emerging training priorities within the allied health sector\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Digital health competencies have become increasingly important, with AHPs expected to navigate electronic health records, telehealth platforms, and data analytic tools\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Leadership and management skills are also in demand, as AHPs often take on supervisory roles and migrate into roles that contribute to organisational decision-making\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e with very little training involved. Furthermore, interprofessional education (IPE) has gained prominence, promoting collaborative practice among healthcare professionals to enhance patient outcomes\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterprofessional education (IPE) has been recognised as a strategy to address shared training needs among healthcare professionals. By fostering collaborative learning environments, IPE aims to improve communication, teamwork, and understanding of professional roles, ultimately enhancing patient care\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The World Health Organisation's Framework for Action on Interprofessional Education and Collaborative Practice advocates for the integration of IPE into health professional curricula, emphasising its role in improving healthcare delivery\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Implementing IPE requires a clear understanding of shared training needs across professions, which can be effectively identified through comprehensive TNA processes. The aim of the study was to undertake a training needs analysis of AHP\u0026rsquo;s working within the Australian public health sector and to identify similarities and differences in training needs across professions, as well as by years of experience, age, qualification, work setting and geographical region. This research seeks to determine whether demographic factors meaningfully influence workplace training needs, to better inform the design of targeted, evidence-based development programs that strengthen allied health professional capabilities and ultimately enhance the quality and efficiency of healthcare delivery.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eDesign, setting and participants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis national study used an analytical cross-sectional design conducted between May and August 2024. An online, self-administered questionnaire was distributed via email across participating public sector health districts. All AHP\u0026rsquo;s as defined by Allied Health Professions Australia\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e were considered eligible, whilst allied health assistants, health promotion officers, pathologists and welfare officers were excluded as their training needs were considered different. All participants in the study provided informed consent, indicating they understood the survey was anonymous and voluntary in nature prior to completion. Only participants who completed the survey in full were included in the analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTraining needs of AHP were measured using the World Health Organisation\u0026ndash;adopted Hennessy Hicks Training Needs Analysis Questionnaire (HHTNA-Q), a self-reported, close-ended structured questionnaire\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The HHTNA-Q was chosen for its psychometric testing for validity and reliability and its use previously in the healthcare sector\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The instrument is semi-opaque, meaning respondents were less likely to be able to distort their response and obtained data should accurately reflect their training requirements\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The questionnaire includes a set of 30 tasks that are relevant to the role of AHP\u0026rsquo;s, categorised into five superordinate domains: research/audit, communication/teamwork, clinical tasks, administration and management/ supervisory tasks. Respondents rate each of these tasks along a 7-point Likert scale according to two criteria: how important the task is to their job, and how well they feel they are currently performing the task. The greater the difference in the two ratings (importance minus performance) indicates the greater the training need for that task. There exists an allowance in the questionnaire design for an additional 10 items without invalidating the tool\u0026rsquo;s psychometric properties\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. An additional 10 questions were developed from thematic reviews of the literature and previous local training needs data to ensure they had some validity, and described tasks considered contemporary and relevant to AHP\u0026rsquo;s. Subsequently, these questions were categorised into the domains \u0026lsquo;utilisation of digital health\u0026rsquo; and \u0026lsquo;leadership / continuous improvement\u0026rsquo;, which reflected core training priorities of health professionals reported in literature from the last five years\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Scale reliability analysis was completed to confirm the 10 questions added did not impact the reliability of the tool.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarise importance and performance scores across the 40 tasks. Training needs were initially explored by calculating mean gap scores (importance minus performance) for each task and visualised using an importance-performance matrix. Paired-samples t tests were used to assess whether importance ratings significantly exceeded performance ratings and effect sizes were calculated using Cohen\u0026rsquo;s d to assess the magnitude of discrepancy between the two ratings. To explore the predictability of demographic variables, multiple linear regression models were constructed using mean performance score as the dependent variable. Each model included demographic variables (profession, age group, years of experience, highest qualification, work setting and work region). Multivariate analyses of variance (MANOVA) were conducted across all seven domains to test for differences in training need ratings using the same demographic factors. All analyses were conducted using IBM SPSS Statistics (Version 29) with statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 2436 AHPs completed the survey from across five Australian states and one territory. The majority of participants were female (83%), between the ages of 28\u0026ndash;43 (58%), had 11 years or more profession-based experience (58%), were working in a metropolitan region (76%) and a hospital setting (78%). A full demographic profile of participants is provided in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert\u003c/em\u003e Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScale reliability analysis demonstrated strong internal consistency for both the original 30-item HHTNA-Q (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.93) and the modified 40-item questionnaire (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.94). Paired-sample t-tests using the mean importance and performance task scores showed all comparisons were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and indicated consistency in the direction (performance\u0026thinsp;\u0026lt;\u0026thinsp;importance) and magnitude of the differences observed. This was further supported by most tasks scoring a positive Cohens d value, with effect size ranging from 0.06 (T29 \u0026ndash; Undertaking administrative activities) to 1.12 (T36 - Managing a healthy work-life balance) suggesting a diverse range of training need priorities across the 40 tasks (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e[Insert\u003c/em\u003e Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003ehere]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor better visuality of the results, an Importance-Performance matrix was developed (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with the combined mean importance (y-axis\u0026thinsp;=\u0026thinsp;5.74) and performance (x-axis\u0026thinsp;=\u0026thinsp;5.17) scores across all 40 tasks serving as the thresholds for the quadrant axes. The distribution of tasks revealed 15 items fell within quadrant one (low importance/low performance), four items within quadrant two (high importance/low performance), 20 items within quadrant three (high importance/high performance), and one task was in quadrant four (low importance/high performance). The four tasks identified as high importance/low performance were: T12 \u0026ndash; Accessing relevant literature for your clinical work, T36 \u0026ndash; Managing a healthy work-life balance, T37 \u0026ndash; Developing more effective ways to get results, and T38 \u0026ndash; Identifying an opportunity for quality improvement.\u003c/p\u003e\n\u003cp\u003eTo examine whether demographic characteristics predicted perceived training need priority, separate linear regression models were conducted for each task in the high importance/low performance quadrant, using the mean score difference between importance and performance as the dependent variable. The initial model included age, gender, First Nations status, disability status, highest qualification, profession, years of experience, work setting and work region (based on the Modified Monash Model - MMM classification). Preliminary analyses indicated that gender, disability status, and First Nations status did not contribute significantly to any model and were excluded to reduce overfitting and improve model parsimony. All four task models were statistically significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level, indicating that the included demographic variables collectively explained a significant proportion of the variance in training need scores (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The strongest model was for Task T37 (F\u0026thinsp;=\u0026thinsp;26.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, adjusted R\u0026sup2; of 0.049). This model identified age, highest qualification, work region, and profession as significant predictors of perceived training gaps. Task T12 also demonstrated a meaningful model fit, (F\u0026thinsp;=\u0026thinsp;21.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, adjusted R\u0026sup2; = 0.040), and showed significant influence from age, highest qualification, and work region. For Task T36, the model explained 3.9% of variance (adjusted R\u0026sup2; = 0.037), with significant predictors including age and work region (F\u0026thinsp;=\u0026thinsp;19.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Task T38 yielded similar findings, (F\u0026thinsp;=\u0026thinsp;20.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, adjusted R\u0026sup2; = 0.039), with highest qualification, years of experience, and work region emerging as significant predictors. Across all four tasks, work region (MMM classification) was a consistent and significant demographic factor associated with perceived training need gaps, indicating variation in priorities based on rurality or remoteness. Age and highest qualification were also frequent predictors, highlighting their relevance in shaping training perceptions. Effect sizes were modest at best across all 4 high importance/low performance tasks (adjusted R\u0026sup2; = 0.037\u0026ndash;0.049), suggesting targeted training interventions may benefit from demographic tailoring, particularly across regional and remote geographic settings.\u003c/p\u003e\n\u003cp\u003eA series of multivariate analyses of variance (MANOVAs) were conducted to examine the influence of demographic characteristics including age group, profession, years of experience, highest qualification level, primary work location, and geographical work region on training needs across seven professional practice domains. Training needs were calculated using mean importance\u0026ndash;performance gap scores for each domain, with higher scores indicating greater perceived need.\u003c/p\u003e\n\u003cp\u003eInitial two-way MANOVAs were performed to assess potential interaction effects between key demographic variables (e.g. age \u0026times; experience); however, no statistically significant interaction terms were identified (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all models). Accordingly, separate one-way MANOVAs were conducted for each demographic factor. Statistically significant multivariate effects were observed across all demographic variables examined. Specifically, significant effects were found for age group (F(21, 6978)\u0026thinsp;=\u0026thinsp;6.455, partial \u0026eta;\u0026sup2; = 0.018); years of experience (F(21, 6978)\u0026thinsp;=\u0026thinsp;10.566, partial \u0026eta;\u0026sup2; = 0.030); profession (F(126, 15857)\u0026thinsp;=\u0026thinsp;2.296, partial \u0026eta;\u0026sup2; = 0.017); qualification level (F(42, 11387)\u0026thinsp;=\u0026thinsp;4.235, partial \u0026eta;\u0026sup2; = 0.012); work setting (F(14, 4422)\u0026thinsp;=\u0026thinsp;3.770, partial \u0026eta;\u0026sup2; = 0.012); and work region (F(42, 11387)\u0026thinsp;=\u0026thinsp;4.602, partial \u0026eta;\u0026sup2; = 0.013). While all multivariate effects reached statistical significance (all \u0026lt;\u0026thinsp;0.001), effect sizes were generally small, suggesting modest differences in training needs between demographic subgroups. The strongest observed effect was for years of experience.\u003c/p\u003e\n\u003cp\u003eFollow-up univariate ANOVAs were conducted to explore domain-level effects in more detail. These analyses demonstrated that demographic factors influenced perceived training needs across all seven domains to varying degrees (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Highest qualification level and years of experience emerged as the most consistent predictors across domains, particularly in the research/audit and communication/teamwork domains. The largest individual effect sizes were observed for work region in the leadership/continuous improvement domain (partial \u0026eta;\u0026sup2; = 0.048) and the management/supervisory domain (partial \u0026eta;\u0026sup2; = 0.036). In contrast, administrative tasks and digital health utilisation domains showed relatively minimal variation by demographic subgroup, indicating that training needs in these areas may be more universally experienced across the allied health workforce.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003ePost hoc comparisons were performed using Tukey\u0026rsquo;s Honestly Significant Difference test where omnibus ANOVA results indicated statistically significant group differences. In the research and audit domain, significant differences were observed across age, qualification, profession, work setting, and work region. Younger professionals aged 18\u0026ndash;27 years reported significantly higher training needs than those aged 44 years and older. Respondents with diploma- and bachelor-level qualifications reported greater needs than those with doctoral qualifications. Several professional groups (Groups 2, 4\u0026ndash;10, 13\u0026ndash;14) reported higher training needs than Profession Group 25, although Group 25 consistently reported lower scores across all domains (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). This pattern suggests that many of the observed post hoc differences may be attributed to the overall low self-assessments reported by Group 25, rather than notably elevated needs among other professions. Higher training needs were also found among those working in hospital, community, or primary care settings, compared to those in addiction and mental health services. Professionals located in very remote areas (MMM 7) reported higher needs than those in metropolitan (MMM 1) and regional (MMM 4) areas.\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the communication and teamwork domain, younger respondents again reported higher needs than all other age groups. Interestingly, those aged 28\u0026ndash;43 years rated their needs lower than those aged 44 and older. Experience showed a similar pattern, with professionals who had five or fewer years of experience reporting greater needs than those with more than six years. While differences across qualification levels were modest, doctoral-qualified professionals again reported the lowest needs. Regionally, respondents in MMM 4 reported greater training needs than those in MMM 1, whereas MMM 7 reported lower needs than MMM 2.\u003c/p\u003e\n\u003cp\u003eFor clinical tasks, younger and less experienced AHPs again reported significantly greater training needs. Bachelor-qualified professionals reported higher needs than doctoral-qualified peers. Regional differences were also observed, with MMM 3 reporting greater needs than MMM 7. Profession-level comparisons were again influenced by the lower mean scores observed in Group 25.\u003c/p\u003e\n\u003cp\u003eIn the administrative tasks domain, no statistically significant differences were detected between professional groups during post hoc testing.\u003c/p\u003e\n\u003cp\u003eIn the management and supervisory tasks domain, middle-aged AHPs (28\u0026ndash;59 years) reported greater training needs than the youngest age group (18\u0026ndash;27 years). Qualification level again differentiated need, with bachelor-level respondents reporting higher needs than those with doctoral-level education. Regionally, those working in MMM 2 reported higher needs than those in MMM 6 and MMM 7, indicating potentially reduced emphasis on supervisory development in more remote areas.\u003c/p\u003e\n\u003cp\u003eIn the digital health utilisation domain, professionals with honours or master\u0026rsquo;s degrees reported greater training needs than doctoral-qualified peers. Regionally, MMM 3 reported greater needs than MMM 7, and professionals working in addiction and mental health services reported higher needs than those in hospital, primary care, or community settings.\u003c/p\u003e\n\u003cp\u003eIn the leadership and continuous improvement domain, respondents from MMM 7 locations reported significantly higher needs than those from all other regional classifications. Once again, younger age and lower levels of experience were associated with higher training needs. Several professional groups reported higher needs than Group 25, consistent with earlier domain comparisons.\u003c/p\u003e\n\u003cp\u003eAlthough many demographic variables demonstrated statistically significant effects, overall effect sizes were small. The pattern of results suggests that perceived training needs are not strongly or consistently predicted by demographic characteristics. Notably, several significant findings appeared to be driven by a small number of outlier groups; particularly Profession Group 25, which consistently reported lower scores across all domains. These findings suggest that while demographic factors such as age, profession, qualification, experience, work setting, and region may influence training needs to a degree, universal training strategies may be more appropriate than narrowly targeted interventions in most cases.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study sought to identify the priority training needs of Australian AHPs working in the public sector and explored whether these needs differed by demographic group or were shared across professions. Prior to this research, there was limited cohesive evidence identifying workforce-wide capability gaps in AHPs or to what extent training needs clustered within or between professional groups. Our findings revealed that demographic characteristics such as profession, age, years of experience, work setting, and work region only explained a small proportion of the variance in training needs. Regression models returned adjusted R-squared values below 0.06, even for tasks with the largest importance-performance gaps. This suggests that while some demographic variables had statistically significant effects; particularly profession and experience, the overall impact was modest at best. Similar observations were observed at the training domain level, indicating limited predictive value of demographic variables for training need differences. These results indicate that workforce development strategies should consider, but not focus on demographic segmentation and instead adopt a task- and domain-based approach, based on identified priority needs. Early-career professionals and those in remote areas may benefit from targeted access and support strategies, but training design should be guided primarily by the competencies required across professional domains, ensuring that educational offerings align with common role expectations and emerging healthcare priorities. Further, other factors not captured in this study, such as organisational culture, access to supervision, leadership opportunities, and time pressures, may have a greater influence\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and should also be carefully considered in workforce training and development initiatives.\u003c/p\u003e\u003cp\u003eA consistent pattern across all 40 tasks was that importance ratings exceeded performance ratings. This discrepancy highlights a perceived gap between what AHPs believe to be essential for effective practice and their current confidence or capability in performing those tasks. Such gaps may reflect limited access to training, supervision, or opportunities to apply skills in the workplace. Prioritising these identified needs through targeted training can enhance both skill development and confidence, which has been shown to improve job satisfaction and workforce engagement\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, research suggests that planned and responsive professional development initiatives contribute positively to workforce retention, particularly when staff perceive that their individual development needs are acknowledged and addressed\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOf particular interest, three of the ten additional questions included in the survey emerged as high-priority training needs: managing a heathy work-life balance, identifying an opportunity for quality improvement and developing more effective ways to get results. These items reflect emerging areas of practice and broader shifts in healthcare delivery, suggesting that contemporary training needs are evolving beyond traditional clinical competencies. Work-life balance has become increasingly critical in the context of rising burnout and workload intensification in healthcare settings\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similarly, value-based healthcare is gaining momentum as systems strive to align outcomes with cost-effectiveness and patient-centred care, necessitating new capabilities in economic and strategic thinking\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The use of digital enabled technology, such as artificial intelligence, in clinical decision-making and workflow optimisation is also accelerating, requiring health professionals to develop digital literacy and ethical reasoning skills\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These findings suggest that contemporary training programs must be agile and future-oriented, equipping AHPs with skills to navigate technological, systemic, and personal challenges in a transforming healthcare landscape.\u003c/p\u003e\u003cp\u003eNotably, the largest average training gaps were observed in the domains of Leadership and Continuous Improvement and Utilisation of Digital Health. These findings are aligned with broader workforce development literature, which underscores the growing need for system-level capabilities in areas such as digital transformation, strategic leadership, and quality improvement\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. As healthcare becomes more complex and team based, AHPs must be equipped with skills to lead change, integrate technology, and drive service innovation. These insights are reinforced by evidence suggesting that AHPs who engage in reflective practice and continuous learning contribute to more efficient, effective, and adaptive health services\u003csup\u003e\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. As such, training initiatives should be designed to develop innovation literacy, design thinking, and improvement science methodologies.\u003c/p\u003e\u003cp\u003eLeadership training emerged as a consistently high priority need across all professional groups. This supports existing literature showing that AHPs are increasingly expected to take on leadership roles without the benefit of formal training, creating challenges in team management and strategic execution\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Targeted leadership development programs can support AHPs to fulfil these expectations, benefiting both patient care and workforce sustainability.\u003c/p\u003e\u003cp\u003eAnother critical finding was the consistent prioritisation of work-life balance training across all demographic groups and professions. This aligns with substantial evidence on the negative impact of stress, burnout, and workload on healthcare professionals\u0026rsquo; well-being and service quality\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The high rating of this item underscores the importance of training that supports psychological resilience, stress management, and sustainable work practices.\u003c/p\u003e\u003cp\u003eImportantly, the relatively small variation in training needs by profession provides a strong argument for leveraging interprofessional education (IPE) models. IPE, which enables professionals to learn with, from, and about one another, has been shown to improve communication, collaboration, and patient outcomes\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Given the consistency of priority training needs across domains and disciplines in this study, IPE offers an efficient and effective approach to training design and delivery. It also ensures consistency in capability development and fosters shared understanding among healthcare teams\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhile this study contributes novel insights into AHP training needs, several limitations should be acknowledged. The cross-sectional, self-reported nature of the data may introduce bias, and although the sample was large and representative of the Australian public health sector workforce, it may not capture the full diversity of experiences across settings. Additionally, training needs are dynamic and context-dependent; thus, several authors have reported that a single survey instrument cannot fully capture the complexity of learning and development requirements\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Future research should incorporate mixed methods approaches, including qualitative interviews and longitudinal studies, to better understand how training needs evolve and how interventions impact capability over time.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis training needs analysis offers a timely and comprehensive examination of the evolving development priorities for Australia\u0026rsquo;s allied health workforce. The findings highlight a clear need to modernise professional development approaches; shifting from traditional, discipline-specific models towards capability-building that reflects the realities of interdisciplinary, digitally enabled, and rapidly transforming healthcare environments.\u003c/p\u003e\u003cp\u003eWhile multivariate analyses revealed statistically significant associations between demographic factors and domain-specific training needs; effect sizes were uniformly small, with no consistent trends observed in post hoc comparisons, suggesting limited practical significance. Some variables such as experience, age and qualification level showed some predictive value, particularly for early-career professionals and those in generalist or remote roles. However, training needs were not systematically elevated across these groups, and no demographic variable emerged as a strong predictor of training demand. Instead, shared challenges were identified across professions, work locations and regions, particularly in the domains of leadership and continuous improvement and digital health literacy. These areas reflect contemporary system-wide shifts in healthcare priorities, including the need for adaptive leadership, data-informed practice, and cross-disciplinary collaboration\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe consistent gap between the perceived importance and self-rated performance across tasks underscores an urgent need for responsive, targeted training. Interprofessional education (IPE) models are especially well-suited to address these shared learning needs in high-priority domains and offer efficiency, consistency, and collaborative benefits. However, training strategies should retain sufficient flexibility to accommodate specific needs driven by context or demographic factors.\u003c/p\u003e\u003cp\u003eTaken together, these insights provide an actionable roadmap for allied health workforce development that is both evidence-informed and aligned with the broader goals of healthcare reform. Investing in targeted, domain specific, and scalable training across all sectors and experience levels, will not only enhance allied health workforce capability, but also contribute to professional satisfaction, service quality, and system sustainability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAHP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAllied Health Professionals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHHTNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQ\u0026ndash;Hennessy Hicks Training Needs Analysis Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIPE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInter\u0026ndash;professional education\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMultivariate analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMMM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eModified Monash Model\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTraining Needs Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was granted by the Metro South Human Research Ethics Committee (HREC/2024/QMS/105880). The study was conducted in accordance with the principles of the Declaration of Helsinki and the International Council for Harmonisation Good Clinical Practice (ICH-GCP) guidelines. Informed consent was obtained from all participants prior to their participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article (and its additional files).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial support was received. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJB conceptualised the study, developed the research protocol, coordinated ethics and governance approvals, led participant recruitment, conducted data collection, performed statistical analyses, and drafted the manuscript. KA contributed to protocol development, coordinated recruitment activities, assisted with data preparation, and contributed to manuscript revision. CL contributed to the development of the protocol, supported data interpretation, and critically reviewed the manuscript for intellectual content. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eThe study manuscript adheres to the International Committee of Medical Journal Editors (ICJME) (http://www.icmje.org) definition of authorship. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the allied health clinicians and health services who participated in this study across Australia.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLizarondo L, Turnbull C, Kroon T, Grimmer K, Bell A, Kumar S, et al. Allied health: integral to transforming health. Aust Health Rev. 2016;40(2):194\u0026ndash;204.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker M, Stanton P, Halvorsen B, Cavanagh J, Bartram T. Allied health professionals: hidden but essential. Research handbook on contemporary human resource management for health care. Edward Elgar Publishing; 2024. pp. 149\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGould D, Kelly D, White I, Chidgey J. Training needs analysis. A literature review and reappraisal. Int J Nurs Stud. 2004;41(5):471\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarkaki A, Malhotra S, Billings R, Theus L. Training needs assessment: tool utilization and global impact. BMC Med Educ. 2021;21(1):310.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHolloway K, Arcus K, Orsborn G. Training needs analysis - The essential first step for continuing professional development design. Nurse Educ Pract. 2018;28:7\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShiri R, El-Metwally A, Sallinen M, Poyry M, Harma M, Toppinen-Tanner S. The Role of Continuing Professional Training or Development in Maintaining Current Employment: A Systematic Review. Healthc (Basel). 2023;11:21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO'Meara MG, Sobelson RK, Trigoso SM, Kramer RE, McNaughton C, Smartis RJ, et al. Ensuring a competent public health responder workforce: The CDC experience. J Emerg Manag. 2019;17(3):199\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCzabanowska K, Rodriguez Feria P. Training needs assessment tools for the public health workforce at an institutional and individual level: a review. Eur J Public Health. 2024;34(1):59\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKolt G. Development of and support for the rural and remote allied health workforce in Australia Australian Council of Deans of Health Sciences Limited; 2025 22 March 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDalton LM, Hills AP, Jayasinghe S, Strong K, Hyland P, Byrne NM. The Allied Health Expansion Program: Rethinking how to prepare a workforce to enable improved public health outcomes. Front Public Health. 2023;11:1119726.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLonghini J, Rossettini G, Palese A. Digital Health Competencies Among Health Care Professionals: Systematic Review. J Med Internet Res. 2022;24(8):e36414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBradd P, Travaglia J, Hayen A. Leadership in allied health: A review of the literature. Asia Pac J Health Manage. 2017;12(1):17\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMahajan R, Mohammed CA, Sharma M, Gupta P, Singh T. Interprofessional Education: An Approach to Improve Healthcare Outcomes. Indian Pediatr. 2018;55(3):241\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOudbier J, Verheijck E, van Diermen D, Tams J, Bramer J, Spaai G. Enhancing the effectiveness of interprofessional education in health science education: a state-of-the-art review. BMC Med Educ. 2024;24(1):1492.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGilbert JH, Yan J, Hoffman SJ. A WHO report: framework for action on interprofessional education and collaborative practice. J Allied Health. 2010;39(Suppl 1):196\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllied Health Professions Australia. About AHPA n.d [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ahpa.com.au/about-ahpa/\u003c/span\u003e\u003cspan address=\"https://www.ahpa.com.au/about-ahpa/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHicks C, Hennessy D. The use of a customized training needs analysis tool for nurse practitioner development. J Adv Nurs. 1997;26(2):389\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHennessy D, Hicks C. Hennessy-Hicks training needs analysis (TNA) questionnaire and manual2011. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://epapers.bham.ac.uk/3453/\u003c/span\u003e\u003cspan address=\"http://epapers.bham.ac.uk/3453/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrennan K, Woodley L, Winstead C. Bridging the gap: Innovating undergraduate public health nursing education through a service-learning health fair project. Public Health Nurs. 2024;41(4):825\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilgate W, Copley J, Hill J. Failing professional practice placements in allied health: What do we understand about the student experience? A scoping review. Adv Health Sci Educ. 2024;29(1):301\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJarva E, Oikarinen A, Andersson J, Pramila-Savukoski S, Hammar\u0026eacute;n M, Mikkonen K. Healthcare professionals' digital health competence profiles and associated factors: A cross‐sectional study. J Adv Nurs. 2024;80(8):3236\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchwartz R, Rollins A, Grant A. Capability building in multidisciplinary teams: the role of supervision and reflective practice. Int J Health Plann Manage. 2019;34(3):e1252\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRollins A, Schwartz R, Lee A. Organisational culture and professional development in healthcare: a systematic review. J Health Organ Manag. 2021;35(5):619\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003en den Berg TI, Elders LA, de Zwart BC, Burdorf A. The effects of work-related and individual factors on the Work Ability Index: a systematic review. Occup Environ Med. 2009;66(4):211\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHalter M, Pelone F, Boiko O, Beighton C, Harris R, Gale J, et al. Interventions to Reduce Adult Nursing Turnover: A Systematic Review of Systematic Reviews. Open Nurs J. 2017;11:108\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKyndt E, Gijbels D, Grosemans I, Donche V. Teachers\u0026rsquo; everyday professional development: a review of the literature. Teacher Teacher Educ. 2016;55:23\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnson J, Louch G, Dunning A, Johnson O, Grange A, Reynolds C, et al. Burnout mediates the association between depression and patient safety perceptions: A cross-sectional study in hospital nurses. J Adv Nurs. 2017;73(7):1667\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAskari R, Rafiei S, Akbari R, Ebrahimi EH, Dehghani A, Shafii M. The relationship between work-life balance and quality of life among hospital employees. Int J Healthc Manag. 2021;14(2):436\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePorter ME, Lee TH. The strategy that will fix health care. Harv Bus Rev. 2013;91(10):50\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTopol E. Deep medicine: how artificial intelligence can make healthcare human again. Hachette UK; 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrenk J, Chen L, Bhutta ZA, Cohen J, Crisp N, Evans T, et al. Health professionals for a new century: transforming education to strengthen health systems in an interdependent world. Lancet. 2010;376(9756):1923\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGreenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res. 2017;19(11):e367.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith L, Jones K, Taylor M. Adapting to change: the importance of continuous improvement in allied health. BMJ Open. 2020;10(5):e037687.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones S, Brown A. Sustaining evidence-based practice: a case for ongoing education. BMC Health Serv Res. 2019;19(1):720.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiller R, Clarke R. Innovation in allied health: harnessing design thinking for better service delivery. Aust Health Rev. 2020;44(6):776\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcKeever M, Brown C. Leading from the middle: experiences of allied health professionals in leadership roles. J Allied Health. 2019;48(3):e67\u0026ndash;373.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaylor M, Williams C, Jordan R. Preparing allied health professionals for leadership: a national scoping study. J Health Leadersh. 2021;13:45\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReeves S, Fletcher S, Barr H, Birch I, Boet S, Davies N, et al. A BEME systematic review of the effects of interprofessional education: BEME Guide 39. Med Teach. 2016;38(7):656\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosen MA, DiazGranados D, Dietz AS, Benishek LE, Thompson D, Pronovost PJ, et al. Teamwork in healthcare: Key discoveries enabling safer, high-quality care. Am Psychol. 2018;73(4):433.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAl-Ismail MS, Naseralallah LM, Hussain TA, Stewart D, Alkhiyami D, Abu Rasheed HM, et al. Learning needs assessments in continuing professional development: a scoping review. Med Teach. 2023;45(2):203\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePang M, Sayner A, McKenzie K. Continuing professional development training needs of allied health professionals in regional and rural Victoria. Aust J Rural Health. 2024;32(4):763\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e=2436)\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en\u0026nbsp;\u003c/em\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e310 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2,019 (83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eTransgender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e61 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNon-binary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e23 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNot disclosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e23 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e18-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e288 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e28-43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1,409 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e44-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e627 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e105 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e70-78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e7 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eIdentifies as First Nations person\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e33 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2,375 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNot disclosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e28 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eLiving with a disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e50 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e2,347 (96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNot disclosed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e39 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eHighest qualification\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eCertificate/Diploma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e20 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eBachelor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e823 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eHonours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e616 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMasters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e870 (36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eDoctoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e107 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e\u0026lt;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e164 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e2 \u0026ndash; 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e382 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e6 - 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e503 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003e11+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1,387 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePhysiotherapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e477 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eOccupational Therapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e417 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eSocial Worker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e413 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eDietitian/Nutritionist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e259 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eSpeech Pathologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e228 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePsychologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e180 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e125 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eRadiographer/Medical Imaging Technologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e91 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePodiatrist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e77 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eOrthoptist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e28 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eExercise Physiologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e27 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eSonographer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e27 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eAudiologist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e24 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eGenetic Counsellor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e14 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMedical/Radiation Therapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e14 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eOrthotist/Prosthetist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e11 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMusic Therapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e10 (0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eArt Therapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e8 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eLeisure/Diversional Therapist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e5 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePhysicist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eState/Territory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNew South Wales\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e937 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eQueensland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e661 (27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eWestern Australia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e383 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eVictoria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e234 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eSouth Australia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e147 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNorthern Territory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e74 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eAustralian Capital Territory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eTasmania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e0 (-)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003ePrimary work location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eHospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1,731 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eCommunity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e307 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eAddiction and Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e179 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eNot listed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e219 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eWork region (based on Modified Monash Model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e1,845 (75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e56 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e129 (5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e83 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e127 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e8 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 417px;\"\u003e\n \u003cp\u003eMM7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003e188 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e[\u003cem\u003eTable 2 title\u003c/em\u003e] Linear regression analysis of top four prioritised tasks\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTask\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic influence (p\u0026lt;0.05)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e21.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate; age, highest qualification, work region (MMM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eT36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate; age, work region (MMM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eT37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e26.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate; age, highest qualification, work region (MMM), profession\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eT38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e20.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 188px;\"\u003e\n \u003cp\u003eModerate; highest qualification, years of experience, work region (MMM)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Logistic regression results for high importance/low performance tasks (T12, T36, T37, T38), showing the influence of demographic factors on the individual tasks score differences (showing p\u0026lt;0.05). Predictors included in the final modelling were age, work setting, work region (MMM), years of experience, highest qualification, and profession.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cem\u003eTable 3 title\u003c/em\u003e] Univariate analysis by task domain (D1 \u0026ndash; D7)\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartial \u0026eta;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD1: Research/Audit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e17.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e16.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD2: Communication/Teamwork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e20.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e6.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e29.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD3: Clinical tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e23.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e13.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e36.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e6.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD4: Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD5: Management/Supervisory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e13.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e2.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e19.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e15.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD6: Utilisation of digital health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e4.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e5.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 266px;\"\u003e\n \u003cp\u003eD7: Leadership/Continuous improvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eQualification level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eProfession\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYears of experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e8.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e10.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eWork region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e20.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e[\u003cem\u003eTable 3 legend\u003c/em\u003e] Significant effects of demographic variables on training needs across the seven domains (univariate analysis).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Allied health, training needs analysis, capability development, education, inter-professional education, leadership, work-life balance, Hennessey Hicks Training Needs Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7005080/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7005080/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAllied health professionals (AHPs) are pivotal to delivering safe, effective, and collaborative healthcare. Yet, there is limited empirical insight into their current training priorities across core domains of professional practice. In an increasingly digital, team-based health system, identifying profession-specific and shared training needs is essential for designing development strategies that retain and grow a future-ready workforce. This study aimed to map training needs across the Australian AHP workforce and explore whether demographic factors predict perceived gaps between the importance and performance of key professional tasks, both within and across professional groups.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA national cross-sectional survey of AHPs was conducted using the WHO-endorsed Hennessy Hicks Training Needs Analysis Questionnaire. Participants self-rated 30 core and 10 emerging tasks by perceived importance and current performance. Gap scores (importance minus performance) were analysed and visualised using an Importance-Performance matrix to highlight priority areas. Descriptive statistics, multiple regression models and multivariate analysis explored whether variation existed between profession and other demographic variables across seven domains of practice.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 2,436 valid responses, a clear and consistent pattern emerged: AHPs rated all tasks as more important than their current performance, highlighting significant perceived capability gaps. The most pronounced needs were found in leadership/continuous improvement, and digital health domains. Emerging tasks such as managing work-life balance, achieving efficient results and identifying opportunities for quality improvement also ranked highly, reflecting the current landscape of AHP practice. While significant demographic differences including level of experience, highest qualification, profession and regionality were observed across domains, effect sizes were uniformly small, suggesting limited practical significance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study provides the first national snapshot of AHP needs and provides compelling evidence that the future of allied health workforce development lies in shifting from siloed, discipline-specific training to capability-building that supports interdisciplinary, digitally enabled healthcare. Interprofessional education models are particularly implicated to address shared training needs in leadership, digital transformation, and system improvement, particularly for early-career professionals and those in remote roles. These findings offer a practical and timely roadmap for aligning training with the evolving demands of healthcare delivery.\u003c/p\u003e","manuscriptTitle":"Understanding the training need priorities of the Australian Allied Health workforce: a national survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:06:35","doi":"10.21203/rs.3.rs-7005080/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-09T15:47:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-08T08:01:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T10:51:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331621063825718274553772526589591630517","date":"2025-08-21T12:35:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T02:59:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121557166507215864491628619669404808738","date":"2025-08-20T23:47:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262608063570717046636366342193161213718","date":"2025-08-20T03:59:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T01:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144208103600219351900036789626069334797","date":"2025-07-21T22:38:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112684366358982642867488517102139094004","date":"2025-07-19T04:12:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T04:44:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325571815772116855962079400568922824220","date":"2025-07-16T00:55:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62271142373731529064013033620800781267","date":"2025-07-16T00:55:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31964847959214361238325406947348503046","date":"2025-07-14T03:00:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-14T00:49:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-13T22:55:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T09:49:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T22:11:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2025-07-09T22:07:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be49393e-44cd-4dbb-abd3-c647325c09b9","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:01:18+00:00","versionOfRecord":{"articleIdentity":"rs-7005080","link":"https://doi.org/10.1186/s12909-025-08141-3","journal":{"identity":"bmc-medical-education","isVorOnly":false,"title":"BMC Medical Education"},"publishedOn":"2025-11-05 15:57:34","publishedOnDateReadable":"November 5th, 2025"},"versionCreatedAt":"2025-07-18 13:06:35","video":"","vorDoi":"10.1186/s12909-025-08141-3","vorDoiUrl":"https://doi.org/10.1186/s12909-025-08141-3","workflowStages":[]},"version":"v1","identity":"rs-7005080","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7005080","identity":"rs-7005080","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}