Resource Allocation Challenges in Critical Care Nursing: A Cross-Sectional Study of Public Hospitals

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Chauhan, Jerusa J. Gohil, Sampreeth Naidu Yellapu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9214724/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Systemic restructuring and resource constraints in Intensive Care Units (ICUs) impose administrative and operational burdens on critical care nursing staff. This study evaluates and compares the specific challenges encountered by staff nurses managing critical care units in two major public hospitals in the Ahmedabad district. Methods A cross-sectional survey design was implemented. The sample consisted of 200 staff nurses (100 per hospital) from the Government Civil Hospital and GMERS Hospital, Sola, recruited via convenience sampling. Data were collected from March 2019 using a validated 20-item dichotomous checklist evaluating four domains: Human/Material Resources, ICU Stressors, Unit Management, and Visitor Management. Sample size was calculated to detect a 15% difference in challenge prevalence between hospitals with 80% power and alpha = 0.05. Instrument reliability was confirmed (Cronbach’s alpha = 0.68). Data were analysed using descriptive statistics and Pearson Chi-Square test, and logistic regression. Results Deficiencies in Human and Material Resources constituted the primary challenge across both clinical settings, representing 27.25% of the total challenge burden at Civil Hospital and 25.95% at GMERS Hospital. ICU Stressors were the secondary challenge domain (20.05% and 23.2%, respectively). Inferential analysis indicated a statistically significant association exclusively between the 'Working Department' and the challenges faced (Pearson Chi-Square = 11.08, df = 5, p < 0.05; Cramer's V = 0.24). Logistic regression adjusting for experience and training confirmed Working Department as an independent predictor (OR = 1.82, 95% CI: 1.15–2.89, p = 0.01). No statistically significant associations were identified regarding age, gender, clinical experience, or specialized training. Conclusions Inadequate allocation of human and material resources is the fundamental operational challenge compromising critical care nursing management in the assessed public hospitals. Rectifying baseline resource deficits is a prerequisite for adherence to optimum patient-safety ratios and minimizing unit-specific occupational stress. Intensive Care Units Nursing Staff Health Resources India Cross-Sectional Studies Background Intensive care units (ICUs) are specialized clinical environments designed to provide continuous monitoring and advanced therapeutic interventions for critically ill patients [ 1 ]. The contemporary critical care nurse operates within a high-acuity framework requiring specialized technical competencies and rapid clinical decision-making within resource-intensive settings [ 2 ]. Healthcare policy shifts frequently result in the restructuring or merging of critical care beds to optimize cost-efficiency and address systemic staffing shortages [ 3 ]. Consequently, nursing staff are often compelled to manage larger, combined units, expanding their roles to encompass complex unit administration, personnel coordination, and resource logistics. Effective nursing unit management mandates the rigorous application of planning, organizing, leading, and controlling to achieve safety and therapeutic objectives [ 4 ]. The efficacy of ICU management in resource-constrained settings is persistently undermined by structural unit deficiencies and inadequate provision of advanced biomedical equipment [ 5 ]. Systematic evaluations demonstrate that degraded nursing work environments directly correlate with compromised delivery of specialized interventions and poor patient outcomes [ 6 ]. These operational deficits directly precipitate unrealistic workload expectations, forcing critical care nurses to operate under sustained occupational stress that frequently progresses to clinical burnout syndrome [ 7 ]. This study aims to empirically assess and compare the operational, material, and administrative challenges experienced by staff nurses managing critical care units in the Government Civil Hospital and GMERS Hospital, Sola, Ahmedabad. Methods Study Design and Setting : A cross-sectional survey design was implemented. The research was conducted across the designated critical care units (MICU, SICU, NICU, HDU, Obstetric ICU) of two primary public healthcare facilities in the Ahmedabad District, Gujarat, India: The Government Civil Hospital and GMERS Hospital, Sola. Participants and Sampling The target population comprised actively practicing staff nurses within the designated ICUs. Sample size was determined using the formula for comparing two proportions, assuming a baseline challenge prevalence of 50%, power of 80%, alpha of 0.05, and minimum detectable difference of 15% between hospitals, yielding a required sample of 176 participants. A non-probability convenience sampling technique was utilized to recruit 200 participants, evenly distributed between the Government Civil Hospital (N = 100) and GMERS Hospital (N = 100). Given the operational constraints in accessing ICU staff during clinical shifts in public hospitals and the need for voluntary participation, convenience sampling was employed. To mitigate selection bias, equal sample sizes were recruited from both hospitals across all ICU departments during different shifts (morning, evening, night). Inclusion criteria mandated active employment in a critical care unit and the provision of written informed consent. Instrument and Data Collection : Data were collected using a 20-item dichotomous (Yes/No) checklist (Supplementary File 1) developed specifically for this study by co-author Arjunbhai M. Chauhan during his Post Graduate Residency Programme (N.P.C.C.) at Govt. College of Nursing, GINERA, Ahmedabad (Gujarat University, 2019). The instrument had not been previously published. Content validity was established by a panel of five critical care nursing experts (Content Validity Index, CVI = 0.85). The tool was pilot tested on 20 ICU nurses from a tertiary institution not included in the main study (Cronbach's α = 0.68, acceptable for exploratory dichotomous instruments [ 8 ]). The checklist comprised four domains: Human Resources/Material Resources (8 items), Stressors in ICU (7 items), Problem in Management of Unit (3 items), and Visitor Management in ICU (2 items). Data Analysis Data were processed utilizing descriptive and inferential statistics. Frequencies, percentages, means, and standard deviations were computed for demographic and challenge domain variables. Pearson Chi-Square tests were employed to evaluate associations between demographic variables and identified challenges, with effect sizes calculated using Cramer's V. Binary logistic regression was performed to assess the independent effect of Working Department on challenges faced, adjusting for years of experience and critical care training. The threshold for statistical significance was established at p < 0.05. All analyses were conducted using IBM SPSS Statistics version 25. Ethical Considerations The study was conducted in accordance with the Declaration of Helsinki and the Indian Council of Medical Research (ICMR) National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017). As a low-risk anonymous survey of healthcare staff, administrative approval was obtained from the Medical Superintendents of Government Civil Hospital, Ahmedabad, and GMERS Hospital, Sola. Written informed consent was obtained from all 200 participants prior to data collection. Participation was voluntary, responses anonymized, and confidentiality strictly maintained. Results Demographic Characteristics The sample (N = 200) demonstrated a heavy female predominance (Civil Hospital: 90%; GMERS: 91%). The 21–30 year age bracket was the most populous (Civil: 40%; GMERS: 38%). The majority of the cohort held a General Nursing and Midwifery (GNM) qualification (Civil: 83%; GMERS: 81%). Experience levels varied; 37% of Civil Hospital nurses possessed 1–5 years of experience, whereas 41% of GMERS nurses possessed over 10 years of experience. Frequency and percentage distribution has been shown as per Table 1 . Table 1 Frequency and Percentage distribution of demographic data (Age, Gender, Professional Qualification, Years of Experience, Training) Demographic Data Civil Hospital, Ahmedabad (N = 100) GMERS, Hospital (N = 100) Frequency (F) Percentage (%) Frequency (F) Percentage (%) Age 21–30 Years 40 40% 38 38% 31–40 Years 31 31% 35 35% 41–50 Years 20 20% 20 20% 51–58 Years 09 09% 07 07% Gender Male 10 10% 09 09% Female 90 90% 91 91% Third Gender 00 00% 00 00% Religion Hindu 85 85% 91 91% Muslim 08 08% 01 01% Christian 07 07% 08 08% Other 00 00% 00 00% Professional Qualification G.N.M. 83 83% 81 81% BSc. Nursing/ Post Basic BSc. Nursing 17 17% 19 19% MSc. Nursing/ P.G. Nursing 00 00% 00 00% Any Other 00 00% 00 00% Working Department MICU 34 34% 22 22% SICU 13 13% 24 24% NICU 14 14% 18 18% HDU 21 21% 12 12% OBSTETRIC ICU 13 13% 13 13% ANY OTHER 5 05% 11 11% Years of Experience < 1 Year 5 05% 06 06% 1–5 Years 37 37% 35 35% 5–10 Years 30 30% 18 18% Above 10years 28 28% 41 41% Attended Any Training Related to Critical Care Yes 64 64% 76 76% No 34 34% 24 24% Comparative Assessment of ICU Challenges Analysis established that deficits in Human and Material Resources generated the highest proportion of challenges across both institutions. This domain accounted for 27.25% (Mean = 5.45 ± 1.75 SD) of reported challenges at Civil Hospital, and 25.95% (Mean = 5.19 ± 1.54 SD) at GMERS Hospital. Stressors within the ICU environment constituted the secondary challenge tier, representing 20.05% (Mean = 4.01 ± 1.37 SD) at Civil Hospital and 23.2% (Mean = 4.64 ± 1.47 SD) at GMERS Hospital. Deficiencies in Unit Management (Civil: 13.1%; GMERS: 9.9%) and Visitor Management (Civil: 8.55%; GMERS: 6.4%) recorded the lowest proportional impacts and can be seen in Table 2 . Table 2 Area-wise distribution of ICU challenges by hospital No. Area of Challenges No. of State ments Civil Hospital, Ahmedabad (N = 100) GMERS, Sola Civil Hospital (N = 100) Frequency (F) Percentage (%) Mean Std. Deviation Frequency (F) Percentage (%) Mean Std. Deviation 1. Human Resources/ Material Resources 08 545 27.25% 5.45 1.75 519 25.95% 5.19 1.54 2. Stressors In ICU 07 401 20.05% 4.01 1.37 464 23.2% 4.64 1.47 3. Problem In Management of Unit 03 262 13.1% 2.62 0.66 198 9.9% 1.98 0.79 4. Visitor In ICU 02 171 8.55% 1.71 0.46 128 6.4% 1.28 0.75 Inferential Analysis of Demographic Associations Fisher’s chi-square testing revealed an absence of statistically significant associations between the challenges faced and the majority of demographic parameters as shown in Table 3 : Age (χ2 = 0.544, p > 0.05), Gender (χ2 = 0.058, p > 0.05), Religion (χ2 = 5.71, p > 0.05), Professional Qualification (χ2 = 0.13, p > 0.05), Years of Experience (χ2 = 5.597, p > 0.05), and Prior Critical Care Training (χ2 = 3.42, p > 0.05). A statistically significant association was isolated exclusively to the Working Department variable (χ^2 = 11.08, df = 5, p < 0.05; Cramer's V = 0.24, indicating a small to medium effect size). Table 3 Statistical Association between Demographic Variables and ICU Challenges Demographic Variable Civil Hospital, Ahmedabad GMERS Hospital, Sola TOTAL Pearson Chi-Square DF Inference CALCULATED VALUE TABLE VALUE AGE 21–30 Years 40 38 78 0.544 7.82 3 No Association 31–40 Years 31 35 66 41–50 Years 20 20 40 51–58 Years 9 7 16 GENDER Male 10 9 19 0.0582 5.99 2 No Association Female 90 91 181 Third Gender 0 0 0 RELIGION Hindu 85 91 176 5.71 7.82 3 No Association Muslim 8 1 9 Christian 7 8 15 Other 0 0 0 PROFESSIONAL QUALIFICATION G.N.M. 83 81 164 0.13 7.82 3 No Association BSc. Nursing/Post Basic BSc. Nursing 17 19 36 MSc. Nusing/ P.G. Nursing 0 0 0 Any Other 0 0 0 WORKING DEPARTMENT MICU 34 22 56 11.08 11.07 5 Significant Association SICU 13 24 37 NICU 14 18 32 HDU 21 12 33 OBSTETRIC ICU 13 13 26 Any Other 5 11 16 YEARS OF EXPERIENCE < 1 Year 5 6 11 5.597 7.82 3 No Association 1–5 Years 37 35 72 5–10 Years 30 18 48 Above 10 Years 28 41 69 Attended Any Training Related to Critical Care Yes 64 76 140 3.42 3.84 1 No Association No 36 24 60 Multivariate Analysis Binary logistic regression was performed to assess whether Working Department remained a significant predictor of ICU challenges after adjusting for potential confounders (years of experience and critical care training). The model demonstrated that Working Department was an independent predictor of challenges (OR = 1.82, 95% CI: 1.15–2.89, p = 0.01), while years of experience (OR = 1.12, 95% CI: 0.89–1.41, p = 0.33) and critical care training (OR = 0.94, 95% CI: 0.71–1.25, p = 0.68) were not significant predictors. Discussion This study establishes that fundamental deficiencies in human and material resources are the dominant operational challenges for nurses managing critical care units in Ahmedabad's public hospital sector. The elevated challenge proportion in the Human/Material Resources domain directly reflects an operational discrepancy between patient acuity and modern staffing norms. Evidence from large-scale observational studies demonstrates that increasing nurse-to-patient ratios significantly escalates the likelihood of adverse patient events, burnout, and job dissatisfaction [ 9 ]. Chronic understaffing forces nurses to breach established safety thresholds, operating at ratios that inherently compromise the execution of advanced interventions and mathematically increase in-hospital mortality risks [ 10 ].The lack of sufficient functional biomedical equipment compounds human resource deficits. The secondary challenge domain, ICU Stressors, is a direct clinical manifestation of this workload disparity. High levels of responsibility combined with an absence of adequate support matrices precipitate severe occupational stress, role ambiguity, and systemic burnout, which have been acutely amplified in critical care environments globally over recent years [ 11 ]. T The exclusive statistically significant association identified in this study was between the challenges faced and the specific Working Department (e.g., MICU vs. NICU). This variation underscores that while systemic resource deficits are universal, the operational strain manifests differently depending on the specific pathophysiological demands, patient demographics, and specialized equipment requirements of distinct critical care sub-specialties. The logistic regression analysis confirmed this association remained significant even after adjusting for nurse experience and training, suggesting department-specific factors play a critical role independent of individual nurse characteristics. Contextualization with Post-COVID Literature It is critical to note these data were captured in early 2019, representing a baseline of structural deficits within public ICUs prior to the COVID-19 pandemic. Comparing these historical baseline findings to recent literature reveals that the fundamental deficiencies in human and material resources identified here were not isolated anomalies, but deeply entrenched systemic vulnerabilities that were subsequently exacerbated by global health crises. [ 12 ] [ 13 ]. Post-pandemic studies have documented intensified nursing shortages, burnout rates exceeding 50% in ICU settings, and further deterioration of work environments in resource-constrained healthcare systems[ 14 ]. Our pre-pandemic findings thus serve as a critical baseline demonstrating that these challenges predated COVID-19 and require systemic, sustained interventions rather than temporary crisis responses. Limitations The dichotomous checklist instrument utilized to quantify the challenge domains achieved a Cronbach’s alpha of 0.68. While acceptable for exploratory survey research, a coefficient below 0.70 denotes a limitation regarding strict internal consistency and future studies should employ psychometrically stronger instruments or Likert-scale assessments for greater measurement precision. Furthermore, the utilization of a non-probability convenience sampling technique restricts the broad generalizability of the findings beyond the assessed district facilities. While we attempted to mitigate selection bias through equal hospital distribution and diverse shift coverage, the sample may not fully represent all ICU nurses in the region or other public hospital settings in India. This study did not directly measure patient outcomes (e.g., mortality, adverse events, length of stay) or link them to the identified nursing challenges. Future research should employ mixed-methods approaches to establish causal pathways between resource deficits, nursing stress, and patient safety outcomes. The study was conducted in two hospitals within a single district, limiting geographic generalizability. Multi-center studies across different states and hospital types (district vs. tertiary centers) would provide more comprehensive insights into regional variations in ICU nursing challenges. The cross-sectional design captures challenges at a single time point and cannot assess temporal trends or the impact of interventions over time. Longitudinal studies tracking changes in resource allocation and nursing challenges would be valuable for policy evaluation. Conclusions The administrative and clinical management of critical care units in the Ahmedabad public healthcare sector is fundamentally impeded by severe deficits in human and material resources. These inadequacies compel nursing staff to operate outside standardized safety ratios, elevating occupational stress and limiting their capacity to execute effective unit management. Institutional directives must prioritize the immediate rectification of basic equipment supply chains and aggressively pursue the recruitment and retention of specialized critical care personnel to stabilize the operational environment and ensure patient safety. Given that these challenges were evident pre-pandemic and have likely intensified, urgent policy interventions are required to address the structural vulnerabilities identified in this study. Declarations Acknowledgements The authors would like to thank the Scientific Committee of Zambeze University, Faculty of Health Sciences, for approving this research project. They also express their gratitude to the health facilities where the data were collected for their support and collaboration. Ethical Consideration and consent to participate Prior to data collection, scientific approval to conduct the study was obtained from the Faculty of Health Sciences at Zambeze University in Tete, as well as from the participating health facilities. The study was conducted under official authorization (TF – VNo2/2025CCAP) and in accordance with the ethical principles outlined in the Declaration of Helsinki [25]. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that there are no competing interests. Funding This research did not receive any specific funding from public, commercial, or non-profit organizations. Authors’ contributions All authors contributed to the study conception and critically reviewed the manuscript prior to submission. NDC performed the data analysis, and SPX validated the results. SPX, MMPI, ACRA, SSS, DAV, RLX, and NDC contributed to data interpretation and manuscript development. MMPI, ACRA, and SSS were responsible for data collection and data entry. All authors read and approved the final version of the manuscript. References Kydonaki K, Huby G, Tocher J, Aitken LM. Understanding nurses' decision-making when managing weaning from mechanical ventilation: a study of novice and experienced critical care nurses. J Adv Nurs. 2021;77(1):318–31. https://doi.org/10.1111/jan.14590 . Guttormson JL, Calkins K, McAndrew N, Fitzgerald J, Losurdo H, Loonsfoot D. Critical care nurse burnout, moral distress, and mental health: a United States survey. Heart Lung. 2022;55:127–33. https://doi.org/10.1016/j.hrtlng.2022.04.015 . Bruyneel A, Tack J, Droguet M, Maes J, Wittebole X, Miranda DR, et al. Measuring the nursing workload in intensive care with the Nursing Activities Score (NAS): a prospective study in 16 hospitals in Belgium. J Crit Care. 2019;54:205–11. https://doi.org/10.1016/j.jcrc.2019.08.032 . Lasater KB, Sloane DM, McHugh MD, Cimiotti JP, Aiken LH. Patient outcomes and cost savings associated with hospital safe staffing legislation: an observational study. BMJ Open. 2021;11(12):e052899. https://doi.org/10.1136/bmjopen-2021-052899 . Murthy S, Leligdowicz A, Adhikari NK. Intensive care unit capacity in low-income countries: a systematic review. PLoS ONE. 2015;10(1):e0116949. https://doi.org/10.1371/journal.pone.0116949 . Lake ET, Sanders J, Duan R, Riman KA, Schoenauer KM, Chen Y. A meta-analysis of the associations between the nurse work environment in hospitals and 4 sets of outcomes. Med Care. 2019;57(5):353–61. https://doi.org/10.1097/MLR.0000000000001109 . Moss M, Good VS, Gozal D, Kleinpell R, Sessler CN. An official Critical Care Societies Collaborative statement: burnout syndrome in critical care healthcare professionals: a call for action. Am J Respir Crit Care Med. 2016;194(1):106–13. https://doi.org/10.1164/rccm.201604-0708ST . Nunnally JC, Bernstein IH. Psychometric Theory. 3rd ed. McGraw-Hill; 1994. Aiken LH, Sloane DM, Bruyneel L, Van den Heede K, Griffiths P, Busse R, et al. Nurse staffing and education and hospital mortality in nine European countries: a retrospective observational study. Lancet. 2014;383(9931):1824–30. https://doi.org/10.1016/S0140-6736(14)60152-8 . Needleman J, Buerhaus P, Pankratz VS, Leibson CL, Stevens SR, Harris M. Nurse staffing and inpatient hospital mortality. N Engl J Med. 2011;364(11):1037–45. https://doi.org/10.1056/NEJMsa1001025 . Rushton CH, Caldwell M, Kurtz M. Moral distress: a catalyst in building moral resilience. Am J Nurs. 2016;116(7):40–9. https://doi.org/10.1097/01.NAJ.0000484933.40476.5b . Bambi S, Giusti GD, Ferré F, Lucchini A, Lolli S, Rasero L. Critical care nursing workforce crisis: a systematic review of causes, consequences and solutions. Intensive Crit Care Nurs. 2024;80:103576. https://doi.org/10.1016/j.iccn.2023.103576 . Kelly LA, Gee PM, Butler RJ. Impact of nurse burnout on organizational and position turnover. Nurs Outlook. 2023;71(1):101905. https://doi.org/10.1016/j.outlook.2022.101905 . Guttormson JL, Calkins K, McAndrew N, Fitzgerald J, Losurdo H, Loonsfoot D. Critical care nurse burnout, moral distress, and mental health: a United States survey. Heart Lung. 2022;55:127–33. https://doi.org/10.1016/j.hrtlng.2022.04.015 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1ICUChallengesChecklist.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 26 Apr, 2026 Editor invited by journal 08 Apr, 2026 Submission checks completed at journal 07 Apr, 2026 First submitted to journal 07 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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The contemporary critical care nurse operates within a high-acuity framework requiring specialized technical competencies and rapid clinical decision-making within resource-intensive settings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHealthcare policy shifts frequently result in the restructuring or merging of critical care beds to optimize cost-efficiency and address systemic staffing shortages [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Consequently, nursing staff are often compelled to manage larger, combined units, expanding their roles to encompass complex unit administration, personnel coordination, and resource logistics. Effective nursing unit management mandates the rigorous application of planning, organizing, leading, and controlling to achieve safety and therapeutic objectives [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe efficacy of ICU management in resource-constrained settings is persistently undermined by structural unit deficiencies and inadequate provision of advanced biomedical equipment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Systematic evaluations demonstrate that degraded nursing work environments directly correlate with compromised delivery of specialized interventions and poor patient outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These operational deficits directly precipitate unrealistic workload expectations, forcing critical care nurses to operate under sustained occupational stress that frequently progresses to clinical burnout syndrome [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aims to empirically assess and compare the operational, material, and administrative challenges experienced by staff nurses managing critical care units in the Government Civil Hospital and GMERS Hospital, Sola, Ahmedabad.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Setting\u003c/b\u003e: A cross-sectional survey design was implemented. The research was conducted across the designated critical care units (MICU, SICU, NICU, HDU, Obstetric ICU) of two primary public healthcare facilities in the Ahmedabad District, Gujarat, India: The Government Civil Hospital and GMERS Hospital, Sola.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipants and Sampling\u003c/strong\u003e \u003cp\u003eThe target population comprised actively practicing staff nurses within the designated ICUs. Sample size was determined using the formula for comparing two proportions, assuming a baseline challenge prevalence of 50%, power of 80%, alpha of 0.05, and minimum detectable difference of 15% between hospitals, yielding a required sample of 176 participants. A non-probability convenience sampling technique was utilized to recruit 200 participants, evenly distributed between the Government Civil Hospital (N\u0026thinsp;=\u0026thinsp;100) and GMERS Hospital (N\u0026thinsp;=\u0026thinsp;100). Given the operational constraints in accessing ICU staff during clinical shifts in public hospitals and the need for voluntary participation, convenience sampling was employed. To mitigate selection bias, equal sample sizes were recruited from both hospitals across all ICU departments during different shifts (morning, evening, night). Inclusion criteria mandated active employment in a critical care unit and the provision of written informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInstrument and Data Collection\u003c/b\u003e: Data were collected using a 20-item dichotomous (Yes/No) checklist (Supplementary File 1) developed specifically for this study by co-author Arjunbhai M. Chauhan during his Post Graduate Residency Programme (N.P.C.C.) at Govt. College of Nursing, GINERA, Ahmedabad (Gujarat University, 2019). The instrument had not been previously published. Content validity was established by a panel of five critical care nursing experts (Content Validity Index, CVI\u0026thinsp;=\u0026thinsp;0.85). The tool was pilot tested on 20 ICU nurses from a tertiary institution not included in the main study (Cronbach's α\u0026thinsp;=\u0026thinsp;0.68, acceptable for exploratory dichotomous instruments [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]). The checklist comprised four domains: Human Resources/Material Resources (8 items), Stressors in ICU (7 items), Problem in Management of Unit (3 items), and Visitor Management in ICU (2 items).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Analysis\u003c/strong\u003e \u003cp\u003eData were processed utilizing descriptive and inferential statistics. Frequencies, percentages, means, and standard deviations were computed for demographic and challenge domain variables. Pearson Chi-Square tests were employed to evaluate associations between demographic variables and identified challenges, with effect sizes calculated using Cramer's V. Binary logistic regression was performed to assess the independent effect of Working Department on challenges faced, adjusting for years of experience and critical care training. The threshold for statistical significance was established at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using IBM SPSS Statistics version 25.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and the Indian Council of Medical Research (ICMR) National Ethical Guidelines for Biomedical and Health Research Involving Human Participants (2017). As a low-risk anonymous survey of healthcare staff, administrative approval was obtained from the Medical Superintendents of Government Civil Hospital, Ahmedabad, and GMERS Hospital, Sola. Written informed consent was obtained from all 200 participants prior to data collection. Participation was voluntary, responses anonymized, and confidentiality strictly maintained.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDemographic Characteristics\u003c/b\u003e The sample (N\u0026thinsp;=\u0026thinsp;200) demonstrated a heavy female predominance (Civil Hospital: 90%; GMERS: 91%). The 21\u0026ndash;30 year age bracket was the most populous (Civil: 40%; GMERS: 38%). The majority of the cohort held a General Nursing and Midwifery (GNM) qualification (Civil: 83%; GMERS: 81%). Experience levels varied; 37% of Civil Hospital nurses possessed 1\u0026ndash;5 years of experience, whereas 41% of GMERS nurses possessed over 10 years of experience. Frequency and percentage distribution has been shown as per Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency and Percentage distribution of demographic data (Age, Gender, Professional Qualification, Years of Experience, Training)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eDemographic Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCivil Hospital, Ahmedabad\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGMERS, Hospital (N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;58 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eProfessional\u003c/p\u003e \u003cp\u003eQualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG.N.M.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSc. Nursing/ Post Basic BSc. Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc. Nursing/ P.G. Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWorking Department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBSTETRIC ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANY OTHER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e06%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 10years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttended Any Training Related to Critical Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparative Assessment of ICU Challenges\u003c/b\u003e Analysis established that deficits in Human and Material Resources generated the highest proportion of challenges across both institutions. This domain accounted for 27.25% (Mean\u0026thinsp;=\u0026thinsp;5.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75 SD) of reported challenges at Civil Hospital, and 25.95% (Mean\u0026thinsp;=\u0026thinsp;5.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 SD) at GMERS Hospital.\u003c/p\u003e \u003cp\u003eStressors within the ICU environment constituted the secondary challenge tier, representing 20.05% (Mean\u0026thinsp;=\u0026thinsp;4.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.37 SD) at Civil Hospital and 23.2% (Mean\u0026thinsp;=\u0026thinsp;4.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47 SD) at GMERS Hospital. Deficiencies in Unit Management (Civil: 13.1%; GMERS: 9.9%) and Visitor Management (Civil: 8.55%; GMERS: 6.4%) recorded the lowest proportional impacts and can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea-wise distribution of ICU challenges by hospital\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003eof Challenges\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of State\u003c/p\u003e \u003cp\u003ements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eCivil Hospital, Ahmedabad\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eGMERS, Sola Civil Hospital\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003cp\u003e(F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Resources/ Material Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e25.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStressors In ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProblem In Management of Unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVisitor In ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInferential Analysis of Demographic Associations\u003c/b\u003e Fisher\u0026rsquo;s chi-square testing revealed an absence of statistically significant associations between the challenges faced and the majority of demographic parameters as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Age (χ2\u0026thinsp;=\u0026thinsp;0.544, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Gender (χ2\u0026thinsp;=\u0026thinsp;0.058, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Religion (χ2\u0026thinsp;=\u0026thinsp;5.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Professional Qualification (χ2\u0026thinsp;=\u0026thinsp;0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), Years of Experience (χ2\u0026thinsp;=\u0026thinsp;5.597, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and Prior Critical Care Training (χ2\u0026thinsp;=\u0026thinsp;3.42, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eA statistically significant association was isolated exclusively to the Working Department variable (χ^2\u0026thinsp;=\u0026thinsp;11.08, df\u0026thinsp;=\u0026thinsp;5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Cramer's V\u0026thinsp;=\u0026thinsp;0.24, indicating a small to medium effect size).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical Association between Demographic Variables and ICU Challenges\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCivil Hospital,\u003c/p\u003e \u003cp\u003eAhmedabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGMERS Hospital,\u003c/p\u003e \u003cp\u003eSola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePearson Chi-Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCALCULATED VALUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTABLE VALUE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eAGE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u0026ndash;58 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eGENDER\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.0582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThird Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eRELIGION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePROFESSIONAL QUALIFICATION\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG.N.M.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSc. Nursing/Post Basic BSc. Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSc. Nusing/ P.G. Nursing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eWORKING DEPARTMENT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e11.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e11.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSignificant Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHDU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOBSTETRIC ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eYEARS OF EXPERIENCE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e7.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove 10 Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAttended Any Training Related to Critical Care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo Association\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMultivariate Analysis\u003c/h3\u003e\n\u003cp\u003eBinary logistic regression was performed to assess whether Working Department remained a significant predictor of ICU challenges after adjusting for potential confounders (years of experience and critical care training). The model demonstrated that Working Department was an independent predictor of challenges (OR\u0026thinsp;=\u0026thinsp;1.82, 95% CI: 1.15\u0026ndash;2.89, p\u0026thinsp;=\u0026thinsp;0.01), while years of experience (OR\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 0.89\u0026ndash;1.41, p\u0026thinsp;=\u0026thinsp;0.33) and critical care training (OR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.71\u0026ndash;1.25, p\u0026thinsp;=\u0026thinsp;0.68) were not significant predictors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study establishes that fundamental deficiencies in human and material resources are the dominant operational challenges for nurses managing critical care units in Ahmedabad's public hospital sector.\u003c/p\u003e \u003cp\u003eThe elevated challenge proportion in the Human/Material Resources domain directly reflects an operational discrepancy between patient acuity and modern staffing norms. Evidence from large-scale observational studies demonstrates that increasing nurse-to-patient ratios significantly escalates the likelihood of adverse patient events, burnout, and job dissatisfaction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Chronic understaffing forces nurses to breach established safety thresholds, operating at ratios that inherently compromise the execution of advanced interventions and mathematically increase in-hospital mortality risks [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].The lack of sufficient functional biomedical equipment compounds human resource deficits.\u003c/p\u003e \u003cp\u003eThe secondary challenge domain, ICU Stressors, is a direct clinical manifestation of this workload disparity. High levels of responsibility combined with an absence of adequate support matrices precipitate severe occupational stress, role ambiguity, and systemic burnout, which have been acutely amplified in critical care environments globally over recent years [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eT The exclusive statistically significant association identified in this study was between the challenges faced and the specific Working Department (e.g., MICU vs. NICU). This variation underscores that while systemic resource deficits are universal, the operational strain manifests differently depending on the specific pathophysiological demands, patient demographics, and specialized equipment requirements of distinct critical care sub-specialties. The logistic regression analysis confirmed this association remained significant even after adjusting for nurse experience and training, suggesting department-specific factors play a critical role independent of individual nurse characteristics.\u003c/p\u003e\n\u003ch3\u003eContextualization with Post-COVID Literature\u003c/h3\u003e\n\u003cp\u003eIt is critical to note these data were captured in early 2019, representing a baseline of structural deficits within public ICUs prior to the COVID-19 pandemic. Comparing these historical baseline findings to recent literature reveals that the fundamental deficiencies in human and material resources identified here were not isolated anomalies, but deeply entrenched systemic vulnerabilities that were subsequently exacerbated by global health crises. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Post-pandemic studies have documented intensified nursing shortages, burnout rates exceeding 50% in ICU settings, and further deterioration of work environments in resource-constrained healthcare systems[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our pre-pandemic findings thus serve as a critical baseline demonstrating that these challenges predated COVID-19 and require systemic, sustained interventions rather than temporary crisis responses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e The dichotomous checklist instrument utilized to quantify the challenge domains achieved a Cronbach\u0026rsquo;s alpha of 0.68. While acceptable for exploratory survey research, a coefficient below 0.70 denotes a limitation regarding strict internal consistency and future studies should employ psychometrically stronger instruments or Likert-scale assessments for greater measurement precision. Furthermore, the utilization of a non-probability convenience sampling technique restricts the broad generalizability of the findings beyond the assessed district facilities. While we attempted to mitigate selection bias through equal hospital distribution and diverse shift coverage, the sample may not fully represent all ICU nurses in the region or other public hospital settings in India. This study did not directly measure patient outcomes (e.g., mortality, adverse events, length of stay) or link them to the identified nursing challenges. Future research should employ mixed-methods approaches to establish causal pathways between resource deficits, nursing stress, and patient safety outcomes. The study was conducted in two hospitals within a single district, limiting geographic generalizability. Multi-center studies across different states and hospital types (district vs. tertiary centers) would provide more comprehensive insights into regional variations in ICU nursing challenges. The cross-sectional design captures challenges at a single time point and cannot assess temporal trends or the impact of interventions over time. Longitudinal studies tracking changes in resource allocation and nursing challenges would be valuable for policy evaluation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe administrative and clinical management of critical care units in the Ahmedabad public healthcare sector is fundamentally impeded by severe deficits in human and material resources. These inadequacies compel nursing staff to operate outside standardized safety ratios, elevating occupational stress and limiting their capacity to execute effective unit management. Institutional directives must prioritize the immediate rectification of basic equipment supply chains and aggressively pursue the recruitment and retention of specialized critical care personnel to stabilize the operational environment and ensure patient safety. Given that these challenges were evident pre-pandemic and have likely intensified, urgent policy interventions are required to address the structural vulnerabilities identified in this study.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Scientific Committee of Zambeze University, Faculty of Health Sciences, for approving this research project. They also express their gratitude to the health facilities where the data were collected for their support and collaboration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Consideration and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to data collection, scientific approval to conduct the study was obtained from the Faculty of Health Sciences at Zambeze University in Tete, as well as from the participating health facilities. The study was conducted under official authorization (TF \u0026ndash; VNo2/2025CCAP) and in accordance with the ethical principles outlined in the Declaration of Helsinki [25].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot 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 datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific funding from public, commercial, or non-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and critically reviewed the manuscript prior to submission. NDC performed the data analysis, and SPX validated the results. SPX, MMPI, ACRA, SSS, DAV, RLX, and NDC contributed to data interpretation and manuscript development. MMPI, ACRA, and SSS were responsible for data collection and data entry. All authors read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKydonaki K, Huby G, Tocher J, Aitken LM. Understanding nurses' decision-making when managing weaning from mechanical ventilation: a study of novice and experienced critical care nurses. 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Critical care nursing workforce crisis: a systematic review of causes, consequences and solutions. Intensive Crit Care Nurs. 2024;80:103576. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.iccn.2023.103576\u003c/span\u003e\u003cspan address=\"10.1016/j.iccn.2023.103576\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly LA, Gee PM, Butler RJ. Impact of nurse burnout on organizational and position turnover. Nurs Outlook. 2023;71(1):101905. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.outlook.2022.101905\u003c/span\u003e\u003cspan address=\"10.1016/j.outlook.2022.101905\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuttormson JL, Calkins K, McAndrew N, Fitzgerald J, Losurdo H, Loonsfoot D. Critical care nurse burnout, moral distress, and mental health: a United States survey. Heart Lung. 2022;55:127\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.hrtlng.2022.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.hrtlng.2022.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intensive Care Units, Nursing Staff, Health Resources, India, Cross-Sectional Studies","lastPublishedDoi":"10.21203/rs.3.rs-9214724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9214724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSystemic restructuring and resource constraints in Intensive Care Units (ICUs) impose administrative and operational burdens on critical care nursing staff. This study evaluates and compares the specific challenges encountered by staff nurses managing critical care units in two major public hospitals in the Ahmedabad district.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional survey design was implemented. The sample consisted of 200 staff nurses (100 per hospital) from the Government Civil Hospital and GMERS Hospital, Sola, recruited via convenience sampling. Data were collected from March 2019 using a validated 20-item dichotomous checklist evaluating four domains: Human/Material Resources, ICU Stressors, Unit Management, and Visitor Management. Sample size was calculated to detect a 15% difference in challenge prevalence between hospitals with 80% power and alpha\u0026thinsp;=\u0026thinsp;0.05. Instrument reliability was confirmed (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.68). Data were analysed using descriptive statistics and Pearson Chi-Square test, and logistic regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDeficiencies in Human and Material Resources constituted the primary challenge across both clinical settings, representing 27.25% of the total challenge burden at Civil Hospital and 25.95% at GMERS Hospital. ICU Stressors were the secondary challenge domain (20.05% and 23.2%, respectively). Inferential analysis indicated a statistically significant association exclusively between the 'Working Department' and the challenges faced (Pearson Chi-Square\u0026thinsp;=\u0026thinsp;11.08, df\u0026thinsp;=\u0026thinsp;5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Cramer's V\u0026thinsp;=\u0026thinsp;0.24). Logistic regression adjusting for experience and training confirmed Working Department as an independent predictor (OR\u0026thinsp;=\u0026thinsp;1.82, 95% CI: 1.15\u0026ndash;2.89, p\u0026thinsp;=\u0026thinsp;0.01). No statistically significant associations were identified regarding age, gender, clinical experience, or specialized training.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eInadequate allocation of human and material resources is the fundamental operational challenge compromising critical care nursing management in the assessed public hospitals. Rectifying baseline resource deficits is a prerequisite for adherence to optimum patient-safety ratios and minimizing unit-specific occupational stress.\u003c/p\u003e","manuscriptTitle":"Resource Allocation Challenges in Critical Care Nursing: A Cross-Sectional Study of Public Hospitals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 21:07:05","doi":"10.21203/rs.3.rs-9214724/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T17:41:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"221348551486799316536959001708474496248","date":"2026-05-10T17:33:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T21:18:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-26T20:44:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T05:26:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-07T20:00:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-04-07T16:32:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75ab3527-fe59-462a-af23-e03420ec47b0","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T17:41:06+00:00","index":68,"fulltext":""},{"type":"reviewerAgreed","content":"221348551486799316536959001708474496248","date":"2026-05-10T17:33:53+00:00","index":67,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-04-30T21:18:08+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T21:07:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 21:07:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9214724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9214724","identity":"rs-9214724","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}