Exploring how organizational support shapes nurses’ clinical leadership: evidence from Tunisia

This study is grounded in the theoretical framework proposed by Wang et al. (2022), which conceptualizes nursing leadership as the outcome of multiple interrelated dimensions influencing leaders’ capacity to sustain effective performance. The model emphasizes five key components: the work environment, performance, behavior, problem-solving, and transformational role. Together, these dimensions capture both contextual and individual factors shaping leadership effectiveness. The framework assumes that a supportive organizational environment, characterized by adequate resources, empowerment, and recognition, enhances nurses’ performance, interpersonal behaviors, and problem-solving abilities, thereby fostering transformational and sustainable clinical leadership. This model provides a comprehensive lens for examining how organizational support contributes to strengthening clinical leadership among nurses in the Tunisian healthcare context [17].

To address the aim of this study, a cross-sectional study was conducted. Participants were recruited from both public and private healthcare facilities located in three Tunisian cities (Sousse, Monastir and Mahdia). Data collection was conducted over a two-month period (January-February, 2025).

The target population consisted of registered nurses employed in both public and private healthcare institutions within the Sahel region of Tunisia, encompassing seven hospitals located across the three main cities. To ensure that participants possessed sufficient clinical exposure and insight into leadership and organizational dynamics, eligibility was restricted to nurses with a minimum of two years of professional clinical experience. This criterion was intended to capture nurses who had acquired practical familiarity with workplace processes, interprofessional collaboration, and institutional support structures.

A convenience sampling approach was adopted to recruit participants from a variety of clinical departments. This method facilitated access to a broad range of nursing roles and contexts, thereby enhancing the diversity and representativeness of the sample.

A priori power analysis was conducted to determine the minimum required sample size for a multiple linear regression model [18, 19]. The calculation was based on the formula used for multiple regression, which relies on the effect size index f² derived from f² = R²/ 1- R². By assuming a small-to-medium effect size (f² = 0.05), an alpha level of 0.05, a statistical power of 0.80, and several potential predictors to be examined, the minimum sample size required was approximately 500 participants.

Data were collected using a self-administered, paper-based questionnaire with an average completion time of approximately 15 min. The questionnaire included a brief introduction outlining the research aim, participant involvement, confidentiality assurances, and informed consent procedures, followed by sections on sociodemographic and professional information and two validated scales.

The first section of the questionnaire gathered participants’ personal characteristics such as gender, age, work experience, level of education, previous training on leadership, workload, healthcare facilities status and accreditation, and intensity of care units.

The second and the third sections were dedicated to assessing the clinical leadership among nurses and their POS using the French versions of the Clinical Leadership Survey (CLS), and the Organizational Environment Assessment (OEA) scale, respectively. The two scales were validated in French in a previous study and report acceptable psychometric properties [20].

The CLS measures clinical leadership competencies in nurses providing direct patient care. It was developed and tested among staff nurses in Canadian acute care hospitals. It includes five core dimensions: Clinical expertise, Effective communication, Collaboration, Coordination and Interpersonal relationships [21]. These dimensions are operationalized through 15 items, equally distributed across the five domains and rated on a 5-point Likert scale (ranging from “Strongly Disagree” to “Strongly Agree”). A global score is calculated by summing the item scores, with higher scores reflecting a stronger perception of clinical leadership capacity [21]. The internal consistency index of the overall clinical leadership measure is considered acceptable for the English version of the CLS, with an overall Cronbach’s alpha of 0.86 [21].

French psychometric evaluation demonstrated good internal consistency (Cronbach’s alpha = 0.73) and preliminary construct validity through confirmatory factor analysis [20].

The OEA scale has a Cronbach’s alpha of 0.82, and consists of six items addressing goal sharing, professional recognition, decision-making support, and development opportunities, rated on a four-point Likert scale. The total score is calculated by summing the scores of the six items. A higher score indicates a positive perception of organizational support (i.e., goal alignment, recognition, decision-making support, and opportunities for professional growth), whereas a lower score reflects a negative perception of the organizational environment [22]. French psychometric evaluation of the 6-item OEA has demonstrated good internal consistency (Cronbach’s α = 0.85) and construct validity [20].

Hospital units were categorized into high-, medium-, and low-intensity care units based on patient acuity, clinical complexity, and the level of monitoring and continuity of care required:

High-intensity care units included the Emergency Department, Intensive Care Unit, Recovery Room, Orthopedic Surgery, Cardiology, Internal Medicine, Pulmonology, Nephrology, Oncology, Pediatrics, and high-risk Maternity Units. Medium-intensity care units comprised Gastroenterology, Neurology, General Surgery, Physical Medicine and Rehabilitation, general Pediatric units, and inpatient Psychiatry. Low-intensity care units included Dermatology, Ophthalmology, Rheumatology, Occupational Medicine, Endocrinology, and outpatient Psychiatry.

The workload was calculated using a specific formula, based on the nurse-to-patient ratio. This ratio is obtained by dividing the number of nurses by the number of patients in the unit, then multiplying the result by 100. A lower nurse-to-patient ratio therefore indicates a higher workload [23]. This ratio is a commonly used standardized indicator of staffing conditions in hospital-based research and provides a structural proxy for workload for descriptive and analytical purposes [15, 24, 25]. Because no validated cut-off values exist to classify this indicator into low, moderate, or high workload levels, no categorical stratification was applied, and this ratio was analyzed as a continuous variable. This approach allowed examination of the direction and strength of associations between staffing levels and clinical leadership without imposing empirically unjustified thresholds.

While the clinical implications of this ratio may differ across high-, medium-, and low-intensity units due to variations in patient acuity and monitoring requirements, we used this formula consistently across all units to provide a standardized measure of nursing workload. This was driven by the fact that more specific unit-adjusted workload measures were not available, and no widely accepted method exists to weight workload according to unit characteristics.

Specialized nurses are nurses with additional training in specific fields such as emergency care, intensive care, anesthesia, operating room nursing, pediatrics, and psychiatry, enabling them to provide advanced care in units with higher clinical demands.

Following authorization obtained from the authors of the translated versions of the CLS and the OEA for their use in this study, as well as approval from the administrators of both public and private healthcare facilities. All procedures were conducted in accordance with Helsinki ethical principles [26], including respect for autonomy, fairness, confidentiality, and protection of participants’ rights. Strict measures were implemented to ensure anonymity and data protection. The study protocol was reviewed and approved by the Ethics Committee of the Faculty of Medicine of Sousse (CEFMS 55/2025). Participants were fully informed about the purpose of the study, the voluntary nature of their participation, and their right to withdraw at any time without providing any explanation. Informed consent was obtained from each participant, and strict measures were implemented to ensure the confidentiality and anonymity of all responses, in alignment with international ethical standards for human subjects’ research.

Data collected through the questionnaire were analyzed using SPSS software, version 22. The analysis was conducted in several stages. Descriptive statistics were performed to characterize the sample. These included measures of central tendency (mean, median) after testing for normality using the Kolmogorov–Smirnov test, as well as frequencies and proportions (%).For the CLS analysis, descriptive statistics (mean, minimum, maximum, and standard deviation) were calculated for each item using a five-point scale. Subscale scores were computed by summing the corresponding items, and presented on a 15-point scale. The overall clinical leadership score was calculated as the sum of the five sub-dimensions and expressed on a 75-point scale [21]. For the OEA analysis, similar descriptive statistics were computed using a four-point scale. The overall score for perceived organizational support was obtained by summing the scores of all items and reported on a 24-point scale [22]. Pearson’s correlation coefficient was calculated between the total scores and sub-dimensions of the CLS and OEA to assess the existence and strength of the relationship between clinical leadership and perceived organizational support.

Independent-samples t-tests were used to compare mean CLS scores between two groups while one-way analysis of variance (ANOVA) was employed for variables with more than two categories. Where significant differences were observed, post-hoc comparisons were considered to identify specific group differences. Furthermore, a multiple linear regression analysis was performed to adjust for potential confounding factors and identify independent predictors of clinical leadership. The level of statistical significance was set at 5%.

The perceived workload showed a mean score of 40 on a 0–100 scale (Min = 15, Max = 68). Detailed sociodemographic and professional characteristics are presented in Table 1.

The overall mean CLS score was 54.15 ± 8.64 (range 33–75). Dimension scores were highest for Effective Communication (11.45 ± 2.43) and lowest for Interpersonal Relationships (10.04 ± 2.16) (Table 2).

The average score for perceived work environment was 16.20 ± 3.46, with values ranging from 8 to 24. The relatively high standard deviation indicates a notable variability in perceptions among participants.

Pearson’s correlation analysis showed a moderate positive correlation between overall CLS score and perceived organizational support (r = 0.501, p < 0.001, 95% CI: 0.44–0.56). All CLS dimensions were positively correlated with organizational support, with r values ranging from 0.261 to 0.444 (Table 3).

Significant associations were found between clinical leadership and several individual characteristics. Female participants reported significantly higher CLS scores than their male counterparts (p = 0.021), and those who had received previous leadership training also scored higher (p = 0.004). Similarly, nurses with more than 10 years of professional experience demonstrated significantly greater clinical leadership scores compared to those with less experience (p = 0.017). A moderate but significant trend was observed with age, indicating that older participants tended to have higher scores (p = 0.045) (Table 4).

In the multivariable regression model, female gender (B = + 1.84, 95% CI: 0.20–3.48, β* = 0.10, p = 0.028), work experience > 10 years (B = + 1.60, 95% CI: 0.12–3.08, β* = 0.09, p = 0.034), and prior leadership training (B = + 3.10, 95% CI: 1.20–5.00, β* = 0.16, p = 0.002) were significant predictors of CLS score. Age was not a statistically significant predictor at the 0.05 level (B = + 0.15, 95% CI: − 0.01–0.31, p = 0.067). The model explained 28% of the variance in CLS scores (R² = 0.28, F = 7.20, p < 0.001) (Table 5).

This study presents several limitations that should be acknowledged when interpreting the findings. First, the cross-sectional design precludes any causal inference regarding the relationship between clinical leadership and organizational support. While a significant correlation was observed, it is not possible to determine the directionality of this relationship. Longitudinal or experimental studies would be more suitable to establish causal links. Second, the use of a convenience sampling method may have introduced selection bias, as participation was limited to nurses who were available and willing to respond to the questionnaire, possibly leading to an overrepresentation of those more engaged or satisfied with their work environment. Third, data were collected through self-reported questionnaires, which are inherently subject to social desirability and recall biases. Participants may have overestimated their leadership behaviors or perceived organizational support to align with socially accepted norms. Additionally, although validated French versions of the CLS and OEA instruments were used, these scales have not undergone a formal cultural and linguistic validation specifically for the Tunisian nursing context. This may limit the full cultural equivalence of the measurements and should be considered when interpreting the findings. However, French is the primary language of professional communication and documentation in Tunisian hospitals, and additional steps were taken to ensure contextual appropriateness, including a pre-test and reliability assessment.

The nurse-to-patient ratio was applied uniformly across units and may not fully reflect differences in workload due to variations in patient acuity, monitoring requirements, or unit complexity. More granular, acuity-adjusted workload measures were not available, and no widely accepted method currently exists to weight workload according to unit characteristics. Future studies should consider developing validated, unit-specific workload indicators to allow more precise comparisons across hospital departments.

The absence of established cut-off values for the nurse-to-patient ratio precluded the categorization of workload into discrete levels. Consequently, workload was examined as a continuous measure, which limits interpretability in terms of absolute workload severity but allows valid correlational analysis without unjustified classification.

Finally, the study focused on a specific geographic area and may not fully represent nurses working in other regions or healthcare settings across Tunisia, particularly those in under-resourced or rural institutions. Consequently, the generalizability of the results should be considered with caution.

The findings of this study have important implications for nursing practice and organizational management. The positive association between clinical leadership and organizational support highlights the need for healthcare managers to foster supportive environments that promote nurses’ leadership capacities. Institutions should implement mentorship and leadership development programs to strengthen nurses’ confidence, decision-making, and collaboration skills.

Providing continuous professional development, recognition, and opportunities for participation in decision-making are key strategies to enhance autonomy, engagement, and shared governance. Promoting fairness, transparency, and psychological safety can further improve nurses’ well-being and strengthen leadership behaviors and organizational loyalty.

From a research perspective, future studies should use mixed-method and longitudinal designs to examine causal pathways between organizational support and leadership development. Qualitative approaches are also needed to capture cultural and contextual factors influencing leadership perceptions and practices. Incorporating multidimensional outcomes such as patient safety, job satisfaction, and retention would offer a more comprehensive understanding of how clinical leadership enhances healthcare quality and resilience, particularly within Tunisian and similar healthcare contexts.

Specific practice-oriented strategies can be recommended to enhance clinical leadership capacity. Healthcare institutions should implement structured mentoring programs, pairing less experienced nurses with seasoned leaders to provide guidance, support, and role modeling. In-service leadership training should be offered regularly, including workshops on decision-making, communication, team coordination, and conflict resolution. Additionally, establishing regular feedback mechanisms, such as performance reviews, peer-assessment sessions, and leadership competency checklists, can help nurses monitor their progress and identify areas for improvement. These actionable strategies can foster leadership development, improve team collaboration, and ultimately strengthen the overall quality of patient care within the organization.