Support Vector Machine-Based Prediction Model for Healthcare Workforce Transition Success Under Decentralization

Study design and population

This was a cross-sectional study that focused on quantitative analysis, complementing a previously published qualitative investigation from our larger project on Fringe Benefits, Welfare, and Career Paths of Personnel in Health Promotion Hospitals under Provincial Administrative Organization (published elsewhere), conducted between March to October 2023. The study aimed to develop predictive models using Support Vector Machine (SVM) analysis to identify factors influencing workforce transition success during Thailand’s healthcare decentralization process.

Sampling strategy

Eight provinces were strategically selected from Thailand’s 77 provinces, representing three levels of healthcare decentralization implementation: low (less than 50% of districts within the province had transferred healthcare facilities to local organizations), moderate (50-99% of districts had completed transfers), and full implementation (all districts within the province had completed transfers to local organizations). These levels were represented by four, two, and two provinces respectively. Sample size was calculated using population proportion estimation (95% confidence interval, ±5% precision) with 15% adjustment for non-response, yielding 430 participants.

Survey instrument

A validated structured questionnaire was developed comprising two main sections. The first section collected demographic and organizational characteristics, including participants’ age, marital status, education level, monthly income, work experience, current position, facility staff headcount (pre- and post-decentralization), number of registered nurses, and facility capacity classification. The second section assessed satisfaction across benefits (19 items) and welfare (11 items) domains, evaluating aspects such as compensation, career advancement, and professional development opportunities using a 5-point Likert scale (1 = very dissatisfied to 5 = very satisfied). The instrument demonstrated strong psychometric properties, with an Item-Objective Congruence Index of 0.8-1.0 for content validity and a Cronbach’s alpha coefficient of 0.96 from pilot testing with 30 non-study healthcare facilities.

Ethical considerations

The study protocol was approved by the Ethics Committee for Human Research of PCKCN (approval number: REC No. 13/2566, dated March 23, 2023). All participants provided informed consent prior to data collection.

Support vector machine model development

The study used SVM analysis in R statistical software (version 4.0.2, e1071 package) to develop a predictive model for workforce transition success. Model development included data preprocessing through standardization of 37 predictor variables spanning demographic factors, benefits, and welfare domains. Four kernel functions (linear, radial basis function, polynomial, and sigmoid) were evaluated to determine optimal model performance. The linear kernel function was selected based on comparative performance metrics:

Model performance was assessed using three metrics: accuracy for measuring overall correct classification rate, sensitivity for assessing true positive rate of successful transitions, and specificity for evaluating true negative rate of unsuccessful transitions. Feature importance analysis was subsequently performed using the weight vectors of the selected kernel model to identify key predictors of successful transitions.

Demographic characteristics

Of the 430 healthcare personnel studied, the majority were female (78.60%, n=338) with a bimodal age distribution peaking at 25-35 years (34.88%, n=150) and over 45 years (33.49%, n=144). More than half were married (56.51%, n=243), and nearly three-quarters held bachelor’s degrees (71.16%, n=306). Professional experience was substantial, with approximately one-third having over 20 years of service (32.79%, n=141). The workforce composition primarily comprised public health officers (23.02%, n=99) and registered nurses (10.47%, n=45), with most personnel (58.14%, n=250) serving in medium-sized sub-district health promoting hospitals.

Predictive factors for workforce transition success

Feature importance analysis was conducted using SVM methodology to identify key determinants of successful workforce transitions. The relative contribution of each predictor was evaluated through examination of standardized feature weights derived from the SVM model coefficients. The target variable was defined as successful transition based on improvements in personnel satisfaction across benefits, welfare, and career advancement domains after transferring to work under the Provincial Administrative Organization.

Feature importance analysis of the 37 predictor variables (10 demographic/organizational, 16 benefits, and 11 welfare variables) using the linear kernel SVM model revealed the relative importance of predictors as shown in Table 1.

Analysis of feature weights derived from the SVM model identified ten key predictors of workforce transition success ( Table 1), with coefficients ranging from 0.427 to 0.190. Financial considerations demonstrated the strongest predictive power, with competitive compensation and benefits (Benefits5, coefficient=0.427) emerging as the primary determinant. Career development opportunities (Welfare30, coefficient=0.358) ranked as the second most influential predictor, suggesting that successful transitions are driven by both immediate financial incentives and long-term professional growth prospects. Among demographic characteristics, educational qualification (coefficient=0.236) emerged as a significant predictor, highlighting the role of individual capacity in transition outcomes. The hierarchical distribution of feature weights provides evidence-based guidance for prioritizing workforce management interventions in decentralized healthcare systems.

Kernel performance summary

Based on the five highest-ranked predictors (feature weights 0.427-0.252) identified through SVM analysis, we evaluated classification performance using four different kernel functions (linear, RBF, polynomial, and sigmoid). These key predictors encompassed competitive compensation (Benefits5), career development opportunities (Welfare30), promotion processes (Benefits4), hazardous work compensation (Benefits15), and educational opportunities (Welfare20). Table 2 presents the comparative performance metrics, where the linear kernel demonstrated superior performance with optimal accuracy and balanced sensitivity-specificity trade-off. While the RBF kernel showed comparable results, the linear kernel’s combination of performance and simplicity made it the preferred choice for our workforce transition prediction model.

Methodological considerations and model performance

The SVM approach was selected for its robust discriminative power in classification tasks involving multiple domains. The model’s demonstrated accuracy of 71.43% indicates moderate but reliable predictive power, comparing favorably with recent healthcare management studies (Lee et al., 2022; Maghami et al., 2023). Using the five highest-ranked predictors (feature weights 0.427-0.252), the linear kernel demonstrated superior performance (71.43% accuracy) compared to RBF (69.92%) and polynomial (67.67%) kernels, suggesting effective linear modeling of transition success predictors. The asymmetric performance metrics (specificity: 85.37%, sensitivity: 49.02%) indicate particular strength in identifying potential transition challenges, though with room for improvement in detecting successful transitions.

Practical implications for workforce management

These findings offer generalizable lessons for several countries implementing healthcare decentralization, specifically in predicting and managing successful personnel transfers. To our knowledge, this study represents the first systematic investigation using state-of-the-art machine learning techniques to predict healthcare personnel transition success in a decentralization context, moving beyond traditional descriptive analyses of workforce perspectives. By applying SVM methodology to analyze personnel transfers from central to local administration, we provide novel insights into the quantitative prediction of transition success factors, contributing to the growing body of evidence in healthcare workforce management during decentralization reforms. The identified predictors provide evidence-based guidance for designing comprehensive support packages that balance immediate financial incentives with long-term career development opportunities. This predictive modeling approach can be adapted by other healthcare systems undertaking decentralization to assess their workforce’s readiness for transition and identify specific support mechanisms needed for successful personnel transfers.

Limitations and considerations

Several methodological limitations should be considered when interpreting our findings. A key limitation was the absence of formal validation techniques, such as train-test splitting or cross-validation, which could have provided more robust estimates of the model’s generalizability. While our SVM model demonstrated utility in predicting workforce transitions (accuracy: 71.43%), the lack of independent validation data means these performance metrics should be interpreted cautiously. Additionally, our sample size of 430 healthcare personnel, though adequate for basic analysis, may limit the model’s stability and generalizability. Future studies should employ more rigorous validation techniques, potentially including k-fold cross-validation and independent test sets, to strengthen the reliability of prediction metrics. Furthermore, while the linear kernel’s performance suggested straightforward relationships among predictors, validation with larger datasets and more sophisticated sampling techniques might reveal more complex patterns in workforce transition dynamics.

Ethical approval and consent

The study protocol was approved by the Ethics Committee for Human Research of Prachomklao College of Nursing (PCKCN), Praboromarajchanok Institute, Ministry of Public Health, Thailand (approval number: REC No. 13/2566, dated March 23, 2023). All participants provided written informed consent prior to data collection. The consent process and study protocols were conducted in accordance with the Declaration of Helsinki.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the author(s) used Claude 3.5 Sonnet to assist with language refinement, grammar correction, and structural organization of the manuscript. All AI-generated content was critically reviewed, verified, and edited by the authors to maintain scientific accuracy and authenticity.