Knowledge And Perception of Climate Change Impacts on Health Among Staff of Sindh Integrated Emergency and Health Services in Karachi, Pakistan

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Methods: A cross-sectional study was conducted among 218 staff members of Sindh Integrated Emergency and Health Services (SIEHS) using a structured Knowledge, Attitudes, and Practices questionnaire adapted from the United Nations Development Programme climate awareness survey. Data were collected through an online self-administered questionnaire. Descriptive statistics, independent t-tests, ANOVA, and Spearman correlation were applied. Results: Participants demonstrated moderate overall knowledge and perceptions of climate change and its health impacts. Statistically significant differences in knowledge and perception scores were observed across age groups, departments, education levels, and years of experience (p < 0.05). Emergency Medical Department staff and participants with higher education levels showed significantly higher scores. A weak but statistically significant positive correlation was identified between knowledge and perception scores (r = 0.195, p = 0.004). Conclusion: Although SIEHS staff demonstrate moderate awareness of climate-related health risks, gaps remain in perceived preparedness and institutional readiness. Targeted climate-health training and structured preparedness programmes may help strengthen workforce response capacity in climate-vulnerable urban settings. Climate change Health impacts Knowledge and perceptions Emergency health services Figures Figure 1 Introduction Climate change refers to long-term alterations in temperature, precipitation, and weather patterns, primarily driven by human activities such as the burning of fossil fuels, deforestation, and industrial processes (Nations, 2025). These activities release greenhouse gases (GHGs) like carbon dioxide and methane into the atmosphere, leading to climate change that causes extreme weather events, directly influencing human health, particularly vulnerable populations. (Nations, 2025). Climate change (CC) is considered one of the major factors influencing human health and illness in a variety of ways. However, there are certain elements that result in exacerbating the impact of climate change on health, including age, inadequate economic resources, and geographical location. (Prevention, 2024). The vulnerable populations are at an increased risk of health complication including respiratory diseases, cardiopulmonary illnesses, vector-borne diseases, water-borne diseases and mental health challenges due to climatic hazards. (Hira Tariq, 2025) Low- and Middle-income countries are primarily exposed to major climatic events because of their inadequate adaptive capacity concerning integrated healthcare systems and infrastructure. Forecasts suggest that between 2030 and 2050, climate change could lead to an estimated 250,000 more deaths each year from causes such as undernutrition, malaria, diarrheal diseases, and heat stress. The health impacts of climate change are particularly severe in low and middle-income countries (LMICs) where healthcare systems are often under-resourced. (WHO, 2023) The National Disaster Management Authority (NDMA) ranked Pakistan among eighth most vulnerable nations globally. It further states that Pakistan is expected to experience a rise of about 5.3 degrees Celsius in temperature, which is higher than the global average of 3.7 degrees Celsius (URAANpakistan, 2024). Moreover, Climate change exacerbates the threat to areas with mountain ranges, comprising a large volume of fresh water. Floods of 2022 reported approximately 12,000 injuries, 8 million displacements and 1739 deaths (reliefweb, 2022). Furthermore, the patterns from 2022 floods highlighted 7,000 cholera cases and 4,500 dengue infections in Sindh alone, while due to collapsed sanitation system and poor infrastructure, an increase in acute fever, malaria, diarrhoeal, and skin infections across multiple flood-affected regions, including Sindh, southern Punjab and Khyber Pakhtunkhwa were reported. While the reports of 2024 floods suggests that Khyber Pakhtunkhwa (KP) and Baluchistan provinces of Pakistan were primarily affected and resulted in causing 117 fatalities and 139 injuries with extensive damage reported, including 464 schools, over 5,800 houses and more than 700 livestock nationwide (reliefweb, 2024). A study revealed that previous flood crises in Pakistan showcased critical disruption in antenatal care, chronic disease management, and childhood immunization services. Pregnant and lactating women were mainly exposed to disrupted maternal care, while adolescent girls experienced trauma and gender-based violence due to the absence of gender sensitive sanitation and psychosocial services (Ayesha Fahim, 2025). Healthcare professionals are categorized among the most reliable and authoritative sources of health information. According to a study, the healthcare workforce believes that climate change adversely impacts human health and endorse their responsibility to advocate for health policies (Antonio Miguel Caraballo-Betancort, 2025). A study conducted among Emergency specialist showcased that most participants recognised a relationship between climate change and health and believed it would impact both their own and their patients’ health (Elzarie Theron, 2025). Similarly, another study suggests that most of the public health professionals considered climate change as a public health issue but lacked information, expertise, and resources to address and combat climate hazards, hence emphasizing the role of healthcare professionals to work on adapting and mitigating strategies for climate change effects. (Antonio Miguel Caraballo-Betancort, 2025). Awareness and perceptions regarding health impacts of climate change play a crucial role in understanding and shaping perceived risks, encouraging preventive behaviours, and strengthening climate adaptive policies (John Kotcher, 2021). The World Health Organization emphasizes that educating individuals is essential for climate preparedness, behaviour adaptation and reducing greenhouse gas emissions. Individuals who acknowledge climate change as an immediate health risk tend to practice preventive and protective behaviours and support relevant policies (John Kotcher, 2021). However, the most evidence emerges from high-income nations, highlighting limited research from Pakistan. Understanding the Knowledge and perceptions of frontline workers, nonclinical staff is therefore critical for more targeted interventions, enhancing preparedness, and supporting policies for climate-vulnerable nations. (Antonio Miguel Caraballo-Betancort, 2025). In Pakistan, particularly among staff of Sindh Integrated Emergency and Health Services in Karachi, there is a notable gap in the literature regarding how healthcare and emergency respondents perceive climate change and its influence on health practices, underscoring the need for context-specific assessment. Rationale of the Study Despite Karachi being a metropolitan city is highly susceptible to climate-related health illnesses, including natural disasters and climate-sensitive diseases. The frontline emergency and healthcare workforce at Sindh Integrated Emergency and Health Services is considered essential to respond to these challenges effectively. The lack of trainings on climate change impact on health, insufficient resources and lack of adequate interventions limit their capacity to effectively manage climate health risks. Gaps in knowledge and perceptions related to climate change impacts on health among Sindh Integrated Emergency and Health Services staff to inform targeted trainings and climate-sensitive responsive health interventions. Aim of the Study This study aims to assess knowledge and measure the perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services (SIEHS). It specifically focuses on frontline health and emergency worker as well as the administrative staff, examining their roles in identifying and responding to climate-related health diseases as well as evaluating the institution's preparedness of SIEHS to address health risks associated with climate change. Research Question What is the Knowledge and Perceptions of Climate Change Impacts on Health among staff of Sindh Integrated Emergency and Health Services in Karachi, Pakistan? Research Objectives: To assess knowledge and perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services. Operational Definitions: Climate Change: Climate change refers to significant and lasting changes in the Earth's temperatures and weather patterns. These changes can be from natural causes (such as large volcanic activity) or from human activities. Health Illness: A state of complete physical, mental, and social well-being and not merely the absence of disease, or infirmity. (WHO 1946). Emergency Respondents: According to the World Health Organization, Emergency respondents are highly skilled teams to support their communities’ immediate needs. It includes Emergency Medical Teams (EMTs), composed of doctors, paramedics, and logistical experts, are crucial responders in crises. Preparedness: The knowledge and capacities developed by governments, response and recovery organizations, communities, and individuals to effectively anticipate, respond to, and recover from the impacts of likely, imminent, or current disasters. Literature Review According to the occupational health literature, climate change adversely impacts the health of workers by exposing them to extreme heat. The highest risk of heat-related illness and mortality is reported to be in workers in agriculture, forestry, fishing, hunting, and construction, which highlights that there is awareness of the risks associated with climate in some occupational groups, but that due to exposure, they are vulnerable. (Nambi Ndugga, 2023) Research on healthcare providers demonstrates that the majority of them acknowledge climate change as a risk to patient health and are interested in participating in the corresponding discussions. But one of the perceived barriers is perceived lack of time, competing priorities, and limited knowledge, which hinder regular communication with patients, which suggests an awareness-practical disjunction. (Tess Wiskel, 2024) Studies carried out on medical and health sciences students reveal high awareness and little knowledge of climate change and its effects on health. Information is mostly obtained through the internet and social media; less usage of scientific journals and formal curricula is mentioned. Though students believe that climate change is a future challenge to the health of patients, a lack of adequate exposure in the curriculum reduces their readiness to deal with its effects in clinical environments. Research into environmental sustainability knowledge indicates that awareness is moderately high, but there is low understanding, and no stable behaviour is taken in terms of sustainability. (J Irlam, 2023) The students are aware of the sustainability concept and are not clear about how it applies to health care, which is a weakness in health curricula concerning environmental health and sustainability. According to population-based studies conducted in Canada, there is concern regarding the health effects of climate change, but it depends on the gender, education, and political orientation. Selective perceptions regarding the health risks were expressed by the respondents who were more interested in water, food, and air quality than mental health or infectious diseases (Nora Casson, 2023) On the same note, a German population survey revealed that there was high recognition of climate change and its health, yet people believe that other people are more affected than they are, thereby displaying cognitive distancing and that there is a need to better communicate risks (Katharina van Baal, 2023) The study, conducted in UK, showed that people who had personal or local experience with flooding and air pollution had reported far greater concern and more negative views of health effects of climate phenomenon, which implies that the experience of living environment has a strong influence on the perception and awareness. (Hilary Graham, 2022). A study conducted in rural Nigeria showed that there is poor knowledge and good attitude to climate change and thus the need to engage in persistent health promotion and dissemination of information to vulnerable communities (Esther O Asekun-Olarinmoye, 2014) Similarly, research conducted in Lusaka Province found that there are significant gaps in stakeholder knowledge, especially concerning indirect health effects. Even though the importance of health-related risks posed by climate change is acknowledged, the need to conduct research and education at a local level is evident. (Mwale, 2025) Lastly, local public health departments in the United States were assessed and either did not possess expertise, staffing and funds or harboured a low institutional priority regarding climate change because of its perceived as an increasing public health issue. This is indicative of structural knowledge and capacity void in the systems of public health.(Edward W. Maibach 2008) Conceptual Framework: The conceptual framework was adapted from published climate change and health impact models (e.g., NSW Health framework), with modifications made to suit the objectives of the present study. This conceptual frame is adapted and modified from existing climate change and health models that briefly explain the conceptual route through which climate-related health illnesses are caused. The original framework showcases direct and indirect climatic exposures leading to mental and physical health outcomes, with vulnerability acting as a modifying factor. In order to incorporate this framework, according to this study, the framework has been modified to be consistent with the research objectives and target population. The adapted model highlights knowledge and perceptions of climate change impacts on health. This study focuses on climatic hazards, including extreme weather events such as heatwaves, pollution, flood all of these hazards are considered as health-related exposures, the outcome of interest includes staff perceptions related to it. Sociodemographic and professional characteristics, including education, role within the Sindh Integrated Emergency and Health Services, years of experience, and prior training, are considered contextual factors that may influence knowledge and perceptions. This adapted framework provides a structured approach to understanding how SIEHS staff perceive the link between climate change and health, while remaining within the scope of a cross-sectional assessment of knowledge and perception METHODS Study Design: A Cross-sectional study design was used in this study to assess knowledge and measure perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services. The data was collected in two phases. The data was collected using a structured and modified questionnaire named the Knowledge, Attitudes, and Practices (KAP) survey, adapted from the UNDP climate change awareness survey. A cross-sectional approach helped to collect the data at a single point in time. Study Setting This study was carried out at two major headquarters of Sindh Integrated Emergency and Health Services (SIEHS) in Karachi, Pakistan. Rescue 1122 Headquarters, Gulshan-e-Iqbal was the first site for data collection comprising of two key departments, Emergency Medical Department and Communication and Command Control Centre (CCC), while the second site is located at Central office, PECHS block 6, Siehs office, where the department of Teletabeeb, Research Development and Education (RDE) and Monitoring, Evaluation, Accountability and Learning (MEAL) departments are stationed. Study Population The study population comprised of Staff working across various departments of Sindh Integrated Emergency and Health Services. These consist of the Emergency Medical Department (Pre-hospital and ambulance care), the communication and command and control centre (emergency call handling and dispatch coordination), and the Research, Development and Education (RDE) Department (Performance monitoring and quality assurance). The accessible population comprised all staff members currently stationed in Karachi and those who were present at the time of data collection and provided informed consent. Eligibility Criteria: Inclusion Criteria: Employee currently stationed at any department of Sindh Integrated Emergency and Health Services in Karachi. Belong to any unit of Sindh Integrated Emergency and Health Services. Have at least 6 months of job experience to ensure exposure to emergency and rescue operations. Willing to provide informed consent and available during the study period. Exclusion Criteria: Staff on leave or temporarily absent during data collection. Retired, resigned, or transferred employees no longer associated with SIEHS. Staff from other cities or regions outside Karachi. Sampling Strategy Sampling Quantitative data were collected using convenience sampling. This approach was employed because the questionnaire was disseminated through office channels, and participants who responded voluntarily were included in the study. Sampling Technique The Mixed approach (Census + Random sampling) is used to ensure differences in department sizes. Due to the unequal size of departments, a mixed sampling approach was carried out. All staff from smaller departments (≤10 members) were included (census), while participants from larger departments (>10 members) were selected using simple random sampling to ensure representativeness and feasibility. Sample Size The sample size for this study was initially calculated using a single-population proportion formula with a 95% confidence level, 5% margin of error, an assumed proportion of 50%, and a 10% anticipated non-response rate. Although the calculated minimum sample was larger, practical constraints during field data collection limited recruitment. Ultimately, 218 participants were successfully enrolled and included in the final analysis. Prior to the main data collection, a pilot study involving 10 participants was conducted to assess the clarity, reliability, and feasibility of the study instrument. Data from the pilot testing were excluded from the final analysis. All eligible participants who provided informed consent during the study period were included . Recruitment Procedure: The recruitment process was initiated after approval from the Ethical Review Committee (ERC) and Institutional Review Board (IRB) of the Shaheed Zulfiqar Ali Bhutto University of science and Technology (SZABIST). The data was collected between October 2025 and December 2025. Participants were recruited using a nonprobability convenience sampling technique. Eligible participants were staff members of the Sindh Integrated Emergency (SIEHS) working in Karachi. Data were collected at a single point in time through an online survey. The survey link was disseminated through official institutional channels and WhatsApp groups following administrative approval. All participants provided electronic informed consent before participation. A subset of respondents from the same sample voluntarily participated in the qualitative component of the study. Participation was entirely voluntary, and confidentiality and anonymity were ensured. Data Collection A self-administered and modified questionnaire comprising 30 closed-ended questions was developed in English and Urdu and then pretested among 12 participants for non-selected staff for assessing content validity, appropriateness, and comprehensibility of the questions. The data was collected through an online Google form. The edited questionnaire was administered by the principal investigator to the 218 participating staff at two different headquarters of Sindh Integrated Emergency and Health Services from December 1st to January 1st. The questionnaire consists of five sections, including socio-demographic information, general knowledge and awareness, experience & observations, perceptions of risk & priorities, health impacts of climate change and training, guidelines & institutional readiness. Data Collection Tools Quantitative Data Collection Tool The quantitative data was collected through a structured, validated questionnaire known as the Knowledge, Attitudes, and Practices (KAP) survey adapted from the UNDP climate change awareness survey, to assess knowledge and measure climate change impacts on health. The five-point Likert scale was used, consisting of 30 closed ended questions covering five sections including socio-demographic information, general knowledge and awareness, experience & observations, perceptions of risk & priorities, health impacts of climate change and training, guidelines & institutional readiness. These sections aim to assess the knowledge regarding climate change impacts on health among SIEHS staff. Evaluation for positive statements: (1) Strongly disagree, (2) Disagree, (3) Neither (4) Agree, (5) Strongly agree, while evaluation for negative statements: (5) Strongly disagree, (4) Disagree, (3) Neither (2) Agree, (1) Strongly agree were used. Tool Validity: The Knowledge, Attitudes, and Practices (KAP) survey is a validated tool based on the UNDP climate change awareness survey. The UNDP KAP Survey on Climate change awareness developed the tool’s content and construct validity through expert review. However, it has not been previously applied in Pakistan; it provided a useful structural framework for developing our questionnaire on climate change knowledge and perceptions. The tool was adaptively modified to reflect local climate change issues in Pakistan. In this study, the instrument was carefully reviewed by the principal investigator for the content relevance and clarity of the language of the tool. This study involving human participants was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. RESULTS Table1: Participant’s Demographic Characteristics The study sample comprised a total of 218 participants for the quantitative phase, as shown in Table 1. Out of 218 participants, 139(63.8%) were females, and 79(26.2%) were males. The age distribution reported that 73 (33.49 %) participants were between 18 and 27, 107 (49.08 %) were between 28 and 37 years of age, 32 (14.68 %) were between 38 and 47 years, and 6 (2.75 %) were above 48 years and above of age. The majority of participants were from the Emergency Medical Department, accounting for 110 (50.46 %), 96 (44.04%) were from the Communication and Command and Control Centre, and 34 (11.3%) were from Monitoring, Evaluation, Accountability and Learning (MEAL), followed by 10 (4.59%) and 2 (0.92%) Teletabeeb and Research, Development, and Education Department (RDE), respectively. The maximum number of participants, 86 (39.45%), had a bachelor's level of education. 47 (21.56%) held a diploma. Respondents with an MS/MPhil included 10 (4.59%). 24 (11.01%) had a secondary level of education. Participants with a primary level of education included 51 (23.39%). Regarding years of experience, 39 (17.89%) have less than 1 year, 95 (43.58%) have 1–3 years, 45 (20.64%) have 4–6 years, and 39 (17.89%) have more than 6 years in the Sindh Integrated Emergency and Health Services. Table 1 Table 1Participant’s Demographic Characteristics Demographic Characteristics Categories Frequency (Percentage) n (%) Age 18–27 years 73 (33.49 %) 28–37 years 107 (49.08 %) 38–47 years 32 (14.68 %) 48–57 years 6 (2.75 %) Gender Female 139(63.8%) Male 79 (26.2%) Department Emergency Medical Department 110 (50.46 %) Communication and Command and Control Centre 96 (44.04%) Research, Development, and Education Department (RDE) 2 (0.92%) Teletabeeb 10 (4.59%) Monitoring, Evaluation, Accountability, and Learning (MEAL) 34 (11.3%) Education Level Primary Secondary MS/MPhil 51(23.39%) 24 (11.01%) 86 (39.45%) Diploma PhD and above 10 (4.59%) 47 (21.56%) Years of Experience Less than 1 year 39 (17.89%) 1–3 years 95 (43.58%) 4–6 years More than 6 years 45 (20.64%) 39 (17.89%) Table 2 Participants Demographic Characteristics The comparison of UNDP KAP survey on climate change awareness and its sub-scales scores based on the Socio-Demographic characteristics of participants is shown in Tables 1 and 2, respectively. Analysis showed that age (F: 2.69, p=0.04), Department of SIEHS staff ( F: 13.48, p=0.000), Educational Level of participants (F:5.41, p=0.0004) and years of experience at Sindh Integrated Emergency and Health Services (F: 3.95, p=0.0091) had a significant impact on knowledge of SIEHS staff. Anova results showed a statistically significant difference in Knowledge scores among different age groups (F: 2.69, p=0.04), showing age as an influencing factor on Knowledge Levels. The score of knowledge regarding climate change impacts on health was higher among those aged between 28 and 37 (12.84± 2.95) and 38 and 47 (13± 4.09) as compared to other age groups. According to the post-Hoc comparison, participants aged 28-37 and 38-47 scored significantly higher than other groups (p<0.05). A statistically significant difference was observed in the Knowledge scores based on the departments participants belonged to (F: 13.48, p=0.000). According to the results, the knowledge scores related to climate change were high among the Emergency Medical Department (164.36±16.20) as compared to other departments. Analysis further revealed a statistically significant difference in the scores of all other departments. A statistical difference in the Communication and Command and Control Centre was significant (165.48±9.17), and participants from Research, Development, and Education (158.69±15.86) had more knowledge of climate change impacts on health. Similarly, the Teletabeeb department had knowledge scores of (155.87±15.77), and the Department of Monitoring, Evaluation, Accountability and Learning (MEAL) had knowledge scores of (162.88±18.06). The mean knowledge score varied significantly across educational levels. Participants with a bachelor’s degree demonstrated the highest mean knowledge score (13.42±3.27), followed by those with an MS/M.Phil. degree (13.8±3.55), participants with a Diploma reported (12.59±3.55) mean scores. While participants holding a secondary degree showcased (13.13±4.09). The lowest mean score was observed among participants with primary education (12.51±3.94). The differences in knowledge scores across educational categories were statistically significant (p < 0.001). The mean knowledge scores increased with years of professional experience. Participants with more than 6 years of experience reported the highest mean knowledge score (13.67± 4.05), followed by those with participants with less than 1 year of experience (13.39±2.62), while the lowest mean score was observed among participants with 1-3 years of experience (12.72±3.64) and participants with years of experience between 4-6 years (12.76±3.84). The findings showed statistically significant results. The demographic variable gender did not show a statistically significant difference in knowledge scores between male and female participants. In addition, no significant difference was observed in overall knowledge scores related to the impacts of climate change on health across gender categories, suggesting that gender was not significantly associated with variations in knowledge or perceptions (p > 0.05). Table 2 Distribution of Knowledge Scores Among Participants by Demographic Characteristics Characteristics Categories Mean (SD) Total Knowledge p-value Statistics Age 18–27 years 12.84± 2.95 p=0.04* F: 2.69 28–37 years 13.17± 3.87 38–47 years 13± 4.09 48–57 years 12.5 ± 3.83 Gender Male 13.00±3.67 p=0.874 t: −0.1586 Female 13.18±1.99 Department Emergency Medical Department 164.36±16.20 p=0.000* F: 13.48 Communication and Command and Control Centre 165.48±9.17 Research, Development, and Education Department (RDE) 158.69±15.86 Teletabeeb 155.87±15.77 Monitoring, Evaluation, Accountability, and Learning (MEAL) 162.88±18.06 Education Level Primary 12.51±3.94 p= 0.0004* F: 5.41 Secondary 13.13±4.09 Bachelors MS/MPHIL 13.42±3.27 Diploma 12.59±3.55 Years of Experience Less than 1 year 1–3 years 4–6 years More than 6 years 13.39±2.62 12.72±3.64 12.76±3.84 13.67± 4.05 p=0.0091* F: 3.95 Table3: Distribution of Perceptions Scores Among Participants by Demographic Characteristics The mean perception scores differed across participants’ demographic and professional characteristics (Table X). Age was significantly associated with perception scores (F = 2.82, p = 0.03), with the highest scores observed among participants aged 18–27 years (9.07±2.47) and the lowest among those aged 48–57 years (7.00±3.83). Significant differences were found in perception scores across departments (F = 13.48, p < 0.001). Participants from the Emergency Medical Department had the highest mean score (9.03±2.33), while lower scores were observed in other departments. (Note: Some department data appear inconsistent and may require verification). Education level also showed a significant association with perception scores (F = 7.27, p < 0.001). Participants with Bachelor’s (9.21±2.20) and MS/MPhil degrees (8.40±2.07) had higher scores compared to those with Primary (7.71±2.58) or Secondary education (6.71±2.16). Diploma holders scored 8.89±2.47. Finally, years of experience were significantly associated with perception scores (F = 3.70, p = 0.01). Participants with less than 1 year of experience had the highest scores (9.62±2.14), followed by those with more than 6 years (8.49±2.52), 4–6 years (8.27±2.28), and 1–3 years of experience (8.11±2.57). These results indicate that age, department, education level, and years of experience significantly influence perception scores among participants, whereas gender does not. Gender was not significantly associated with perception scores (t = −0.9686, p = 0.333); males had a mean score of 8.43±2.51, while females scored 9.18±1.66. Table 3 Distribution of Perception Scores Among Participants by Demographic Characteristics Characteristics Categories Mean (SD) Total GDPBS p-value Statistics Age 18–27 years 9.07±2.47 p=0.03* F: 2.82 28–37 years 8.32±2.63 38–47 years 7.94±1.81 48–57 years 7± 3.83 Gender Male 8.43±2.51 p=0.333 t: -0.9686 Female 9.18±1.66 Department Emergency Medical Department 9.03±2.33 p=0.000* F: 13.48 Communication and Command and Control Centre 7.68±2.51 Research, Development, and Education Department (RDE) ±15.86 Teletabeeb 155.87±15.77 Monitoring, Evaluation, Accountability, and Learning (MEAL) 162.88±18.06 Education Level Primary 7.71±2.58 p= 0.000* F: 7.27 Secondary 6.71± 2.16 Bachelors MS/MPHIL 9.21±2.20 8.4±2.07 Diploma 8.89±2.47 Years of Experience Less than 1 year 1–3 years 4–6 years More than 6 years 9.62± 2.14 8.11± 2.57 8.27±2.28 8.49± 2.52 P= 0.01* F: 3.70 Table 4: Correlation between knowledge and perception scores regarding climate change impacts on health Spearman’s correlation analysis showed a statistically significant weak positive relationship between knowledge and perception scores regarding climate change impacts on health (r = 0.195, p = 0.004), based on responses from 218 participants. Table 4 Correlation between knowledge and perception scores regarding climate change impacts on health Variable 1 Variable 2 r p-value N Knowledge Perceptions r=0.195 p= 0.004 * N=218 Study Strengths This study has several strengths. Firstly, to our knowledge, this study is one of the first quantitative studies conducted among clinical and non-clinical staff, aiming to assess the knowledge and perceptions regarding Climate Change impacts of health among Sindh Integrated Emergency and Health Services staff, which is an emerging topic to address the knowledge and perceptions gap among front-line professionals in high-risk areas of Karachi, Pakistan. The quantitative data allowed us to understand the level of Knowledge and perceptions among SIEHS staff. Secondly, the participation of diverse staff from different departments of Sindh Integrated Emergency and Health Services enhanced the generalizability. The findings from the study could serve as a potential implication and guide for policymakers, particularly in the healthcare and education sectors, can use the findings from this study to design and implement targeted interventions that improve the knowledge, preparedness, and response of SIEHS staff regarding the health impacts of climate change, ultimately contributing to the physical, mental, and social well-being of the communities they serve.. Study Limitations This study has some limitations. Its cross-sectional design prevents establishing causal relationships, and data were self-reported, which may be affected by recall or social desirability bias. The sample was limited to SIEHS staff, which may reduce generalizability to other regions or emergency services. Additionally, qualitative insights were not collected, which could have provided a deeper understanding of staff perceptions. Recommendations It is recommended that targeted training programs be implemented to improve SIEHS staff knowledge of climate change and its health impacts. Policies should integrate climate-related health risks into emergency response planning, and future research should include qualitative and longitudinal studies to better understand staff perceptions and preparedness. Expanding the study to other regions would also enhance generalizability. Here is a short, high-quality, journal-ready Discussion section (concise, integrated, scholarly, and literature-linked) suitable for manuscripts with word limits. It maintains academic rigor, compares findings with other studies, and is formatted as one coherent section. DISCUSSION This study provides one of the first quantitative assessments of knowledge and perceptions regarding the health impacts of climate change among both clinical and non-clinical staff of Sindh Integrated Emergency and Health Services (SIEHS) in Karachi, Pakistan. Conducted in a highly climate-vulnerable urban context, the study addresses a critical research gap by examining climate–health awareness among frontline emergency and health professionals who play a central role in disaster response and population health protection. The findings indicate that SIEHS staff possess moderate overall knowledge and perceptions of climate change and its health consequences, with awareness of key risks such as heat-related illnesses, respiratory diseases, vector- and water-borne infections, and psychological distress during disasters. These results are consistent with international studies among healthcare professionals in high-income and low- and middle-income countries, which similarly report moderate to high climate–health awareness but uneven preparedness and institutional integration (Kotcher et al., 2021; Wiskel et al., 2024; Caraballo-Betancort et al., 2025; Hira Tariq et al., 2025). Significant differences in knowledge and perception scores across age, department, education level, and years of experience reflect structural and professional inequalities in access to climate–health exposure and training. Higher knowledge among Emergency Medical Department and Communication and Command and Control Centre staff aligns with evidence that frontline exposure to climate-related emergencies strengthens climate–health understanding (Kang et al., 2023; Theron et al., 2025). Similarly, the association between higher education and greater knowledge supports findings from China and South Africa, where formal education enhances climate literacy but does not ensure operational preparedness (Yang et al., 2018; Irlam et al., 2023). Higher perception scores among younger staff and frontline emergency workers mirror international research showing that direct experience with environmental hazards and generational exposure to climate discourse increase perceived health risks (Graham et al., 2022; Casson et al., 2023; van Baal et al., 2023). The higher perception among newly employed staff likely reflects recent academic exposure to climate change content, consistent with findings from studies among health students and early-career professionals (Yang et al., 2018; Irlam et al., 2023). The weak but significant correlation between knowledge and perception highlights a critical knowledge–perception gap, indicating that increased awareness does not necessarily translate into stronger risk perception or preparedness. This pattern is widely documented in climate–health literature and underscores the limitations of information-based training alone (Maibach et al., 2008; Kotcher et al., 2021; Wiskel et al., 2024). Overall, these findings emphasize the need for structured climate–health training, experiential learning, and institutional integration of climate resilience frameworks within emergency health systems. Strengthening intersectoral collaboration between healthcare services, disaster management authorities, and environmental agencies is essential for building climate-resilient health systems and protecting vulnerable urban populations. Despite being limited to a single institution, this study provides critical baseline evidence for Pakistan and contributes to the limited literature on climate–health awareness in emergency service systems in low- and middle-income countries. Conclusion This study highlights that SIEHS staff possess varying levels of knowledge and perceptions regarding the health impacts of climate change, with notable gaps in awareness and preparedness. Strengthening staff capacity through targeted training, integrating climate-related health risks into emergency response protocols, and fostering intersectoral collaboration are essential to enhance resilience and ensure effective community health protection. Addressing these gaps will not only improve the readiness of emergency health services but also contribute to safeguarding the physical, mental, and social well-being of the populations they serve in the face of climate-related challenges. Abbreviations SZABIST- Shaheed Zulfiqar Ali Bhutto University of Science and Technology SIEHS- Sindh Integrated Emergency and Health Services GHGs - Greenhouse gases CC- Climate Change LMICs- Low and Middle-Income Countries NDMA- National Disaster Management Authority WHO- World Health Organization EMTs- Emergency Medical Teams CCC- Communication and Command Control Centre RDE- Research Development and Education MEAL- Monitoring, Evaluation, Accountability and Learning KAP- Knowledge, Attitudes, and Practices UNDP- United Nations Development Programme Declarations Ethics approval and consent to participate Ethical approval was obtained from the Ethical Review Committee and Institutional Review Board of Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (SZABIST), Karachi, Pakistan. All participants provided electronic informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this research. Authors’ contributions Shehzeen Niaz conceived the study, developed the methodology, conducted analysis and drafted the manuscript. Rabiya Javed contributed to data collection and manuscript preparation. Suleman Otho supervised the study and critically reviewed the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors would like to thank the staff of Sindh Integrated Emergency and Health Services, Karachi, for their cooperation and participation in this study. We also acknowledge that the questionnaire used in this research was adapted and modified from the Climate Change Knowledge, Attitudes and Practices (KAP) survey developed under the climate change awareness campaign supported by the United Nations Development Programme (UNDP) (Fontenard T., Grenada Climate Change Awareness Survey Report). The original report is publicly available online and served as a valuable reference in developing the study instrument. Author’s Information Not Applicable References Antonio Miguel Caraballo-Betancort, I. M.-T., Blanca Notario-Pacheco, Shkelzen Cekrezi, Ana Perez-Moreno, Maria Martinez-Andres (2025). Health professionals' perceptions of climate change: A systematic review of qualitative studies. Public Health , 245 , 105773. https://doi.org/https://doi.org/10.1016/j.puhe.2025.105773 Ayesha Fahim, H. A. Q., Ahsan Sethi. (2025). Public Health collapse during floods in Pakistan: A call for National & Global Response. Pak J Med Sci , 41 ((10)). https://doi.org/10.12669/pjms.41.10.13355 Edward W. Maibach , A. C., Dennis McBride,Michelle Chuk,Kristie L. Ebi,John Balbus. (2008). Climate Change and Local Public Health in the United States: Preparedness, Programs and Perceptions of Local Public Health Department Directors. PLOS . https://doi.org/https://doi.org/10.1371/journal.pone.0002838 Elzarie Theron, G. Z., Willem Stassen. (2025). Emergency specialists’ perceptions of climate-related health and education in South Africa. Journal of the Colleges of Medicine of South Africa , 3 (1). https://doi.org/https://doi.org/10.4102/jcmsa.v3i1.214 Esther O Asekun-Olarinmoye, J. O. B., Olusola O Odu, Adenike I Olugbenga-Bello, Olugbenga L Abodurin, Wasiu O Adebimpe, Edward A Oladele, Adeleye A Adeomi, Oluwatosin A Adeoye &Ebenezer O Ojofeitimi (2014). Public perception of climate change and its impact on health and environment in rural southwestern Nigeria. Taylor and Francis 5 . https://doi.org/https://doi.org/10.2147/RRTM.S53984 Hilary Graham, A. H., Pete Lampard. (2022). Public Perceptions of Climate Change and Its Health Impacts: Taking Account of People’s Exposure to Floods and Air Pollution. mdpi , 19 ((4)), 2246. https://doi.org/https://doi.org/10.3390/ijerph19042246 Hira Tariq, S. N., Umm-e-Rabab,Sameena Ourangzaib. (2025). Perceptions of medical and public health professionals on climate change and emerging health challenges in Pakistan: a multi-scale approach. BMC Med Educ. , 25 (6). https://doi.org/10.1186/s12909-025-07257-w J Irlam, Z. R., H-A Rother. ( 2023). Student knowledge and perceptions of climate change and environmental sustainability at the Faculty of Health Sciences, University of Cape Town, South Africa. African Journal of Health Professions Education , 15 (1). https://doi.org/https://doi.org/10.7196/AJHPE.2023.v15i1.1659 John Kotcher, E. M., Jeni Miller, Eryn Campbell, Lujain Alqodmani, Marina Maiero, Arthur Wyns. (2021). Views of health professionals on climate change and health: a multinational survey study. Lancet Planet Health , 5 ((5)), e316–e323. https://doi.org/10.1016/S2542-5196(21)00053-X Katharina van Baal, S. S., Peter Schulte ( 2023). Public Perceptions of Climate Change and Health—A Cross-Sectional Survey Study. mdpi , 20 ((2)), 1464. https://doi.org/https://doi.org/10.3390/ijerph20021464 Mwale, M. (2025). Assessment of Knowledge and Perceptions of Climate Change Impacts on Public Health Among Key Stakeholders in Lusaka province, Zambia. EvalHarvest . https://evalharvest.com/pdfs/climate-change-impacts-public-health.pdf Nambi Ndugga, D. P., Samantha Artiga. (2023). Climate-Related Health Risks Among Workers: Who is at Increased Risk? KFF . https://www.kff.org/racial-equity-and-health-policy/climate-related-health-risks-among-workers-who-is-at-increased-risk/ Nations, U. (2025). Climate Action . https://www.un.org/en/climatechange/what-is-climate-change Nora Casson, L. C., Ian Mauro, Karl Friesen-Hughes, Rhéa Rocque (2023). Perceptions of the health impacts of climate change among Canadians. BMC Public Health , 23 . https://doi.org/https://doi.org/10.1186/s12889-023-15105-z Prevention, C. o. D. C. a. (2024). Effects of Climate Change on Health . https://www.cdc.gov/climate-health/php/effects/index.html reliefweb. (2022). Pakistan: Floods - Jul 2022 . https://reliefweb.int/disaster/fl-2022-000254-pak reliefweb. (2024). Pakistan: Floods - Feb 2024 . https://reliefweb.int/disaster/fl-2024-000020-pak Tess Wiskel, T. T. M., Mariel Fonteyn, Kristin Stevens, Chelsea Heberlein, Nathaniel Matthews-Trigg, Caleb Dresser & Aaron Bernstein (2024). Frontline clinic perspectives on climate change, human health, and resilience: a national cross-sectional survey. BMC Primary Care 25 (2024). https://doi.org/https://doi.org/10.1186/s12875-024-02622-y URAANpakistan. (2024). ENVIRONMENT & CLIMATE CHANGE . https://uraanpakistan.pk/climate-change/ WHO. (2023). Climate Change . https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8887385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609917251,"identity":"80261e13-fc1e-4f38-8fc5-09d1396d3436","order_by":0,"name":"Shehzeen Niaz Ali","email":"data:image/png;base64,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","orcid":"","institution":"Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Shehzeen","middleName":"Niaz","lastName":"Ali","suffix":""},{"id":609917252,"identity":"53c5e0b4-f352-44fc-8436-c4a74d819d1f","order_by":1,"name":"Rabiya Javed","email":"","orcid":"","institution":"Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Rabiya","middleName":"","lastName":"Javed","suffix":""},{"id":609917253,"identity":"f68267c0-c88c-4cdd-a8ba-a538dc32f512","order_by":2,"name":"Suleman Otho","email":"","orcid":"","institution":"Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Suleman","middleName":"","lastName":"Otho","suffix":""}],"badges":[],"createdAt":"2026-02-15 16:38:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8887385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8887385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566969,"identity":"c62ef9bf-d6b8-4f64-80fa-2087696c1fe5","added_by":"auto","created_at":"2026-03-27 12:57:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119858,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in \u003cstrong\u003eLiterature Review \u003c/strong\u003esection.\u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8887385/v1/01c569b7bc66307e55716a7e.png"},{"id":105570273,"identity":"9ad6c3af-842b-42a3-a6ad-ebc2bd7f301a","added_by":"auto","created_at":"2026-03-27 13:15:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1394115,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8887385/v1/b859c7c2-d9d5-4269-8e66-de4b03b80852.pdf"},{"id":105456522,"identity":"15c4ce5a-a7b5-43e9-a955-81dabd25261a","added_by":"auto","created_at":"2026-03-26 09:14:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15402,"visible":true,"origin":"","legend":"","description":"","filename":"Responsesheet.docx","url":"https://assets-eu.researchsquare.com/files/rs-8887385/v1/29d335340d187a85d9a6014c.docx"},{"id":105456519,"identity":"80580efd-8322-4daf-a3d7-4e4c8e97fff6","added_by":"auto","created_at":"2026-03-26 09:14:37","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27461,"visible":true,"origin":"","legend":"","description":"","filename":"QUANTITATIVEQUESTIONNAIRE.docx","url":"https://assets-eu.researchsquare.com/files/rs-8887385/v1/c636ff3dd12e416d7bccf167.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Knowledge And Perception of Climate Change Impacts on Health Among Staff of Sindh Integrated Emergency and Health Services in Karachi, Pakistan","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change refers to long-term alterations in temperature, precipitation, and weather patterns, primarily driven by human activities such as the burning of fossil fuels, deforestation, and industrial processes (Nations, 2025). These activities release greenhouse gases (GHGs) like carbon dioxide and methane into the atmosphere, leading to climate change that causes extreme weather events, directly influencing human health, particularly vulnerable populations. (Nations, 2025).\u003c/p\u003e\n\u003cp\u003eClimate change (CC) is considered one of the major factors influencing human health and illness in a variety of ways. However, there are certain elements that result in exacerbating the impact of climate change on health, including age, inadequate economic resources, and geographical location. (Prevention, 2024). The vulnerable populations are at an increased risk of health complication including respiratory diseases, cardiopulmonary illnesses, vector-borne diseases, water-borne diseases and mental health challenges due to climatic hazards. (Hira Tariq, 2025)\u003c/p\u003e\n\u003cp\u003eLow- and Middle-income countries are primarily exposed to major climatic events because of their inadequate adaptive capacity concerning integrated healthcare systems and infrastructure. Forecasts suggest that between 2030 and 2050, climate change could lead to an estimated 250,000 more deaths each year from causes such as undernutrition, malaria, diarrheal diseases, and heat stress. The health impacts of climate change are particularly severe in low and middle-income countries (LMICs) where healthcare systems are often under-resourced. (WHO, 2023)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe National Disaster Management Authority (NDMA) ranked Pakistan among eighth most vulnerable nations globally. It further states that Pakistan is expected to experience a rise of about 5.3 degrees Celsius in temperature, which is higher than the global average of 3.7 degrees Celsius (URAANpakistan, 2024). Moreover, Climate change exacerbates the threat to areas with mountain ranges, comprising a large volume of fresh water. \u0026nbsp;Floods of 2022 reported approximately 12,000 injuries, 8 million displacements and 1739 deaths (reliefweb, 2022). Furthermore, the patterns from 2022 floods highlighted 7,000 cholera cases and 4,500 dengue infections in Sindh alone, while due to collapsed sanitation system and poor infrastructure, an increase in acute fever, malaria, diarrhoeal, and skin infections across multiple flood-affected regions, including Sindh, southern Punjab and Khyber Pakhtunkhwa were reported. While the reports of 2024 floods suggests that Khyber Pakhtunkhwa (KP) and Baluchistan provinces of Pakistan were primarily affected and resulted in causing 117 fatalities and 139 injuries with extensive damage reported, including 464 schools, over 5,800 houses and more than 700 livestock nationwide (reliefweb, 2024). A study revealed that previous flood crises in Pakistan showcased critical disruption in antenatal care, chronic disease management, and childhood immunization services. Pregnant and lactating women were mainly exposed to disrupted maternal care, while adolescent girls experienced trauma and gender-based violence due to the absence of gender sensitive sanitation and psychosocial services (Ayesha Fahim, 2025).\u003c/p\u003e\n\u003cp\u003eHealthcare professionals are categorized among the most reliable and authoritative sources of health information. According to a study, the healthcare workforce believes that climate change adversely impacts human health and endorse their responsibility to advocate for health policies (Antonio Miguel Caraballo-Betancort, 2025). A study conducted among Emergency specialist showcased that most participants recognised a relationship between climate change and health and believed it would impact both their own and their patients\u0026rsquo; health (Elzarie Theron, 2025). Similarly, another study suggests that most of the public health professionals considered climate change as a public health issue but lacked information, expertise, and resources to address and combat climate hazards, hence emphasizing the role of healthcare professionals to work on adapting and mitigating strategies for climate change effects. (Antonio Miguel Caraballo-Betancort, 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAwareness and perceptions regarding health impacts of climate change play a crucial role in understanding and shaping perceived risks, encouraging preventive behaviours, and strengthening climate adaptive policies (John Kotcher, 2021). The World Health Organization emphasizes that educating individuals is essential for climate preparedness, behaviour adaptation and reducing greenhouse gas emissions. Individuals who acknowledge climate change as an immediate health risk tend to practice preventive and protective behaviours and support relevant policies (John Kotcher, 2021). However, the most evidence emerges from high-income nations, highlighting limited research from Pakistan. Understanding the Knowledge and perceptions of frontline workers, nonclinical staff is therefore critical for more targeted interventions, enhancing preparedness, and supporting policies for climate-vulnerable nations. (Antonio Miguel Caraballo-Betancort, 2025). In Pakistan, particularly among staff of Sindh Integrated Emergency and Health Services in Karachi, there is a notable gap in the literature regarding how healthcare and emergency respondents perceive climate change and its influence on health practices, underscoring the need for context-specific assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRationale of the Study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite Karachi being a metropolitan city is highly susceptible to climate-related health illnesses, including natural disasters and climate-sensitive diseases. The frontline emergency and healthcare workforce at Sindh Integrated Emergency and Health Services is considered essential to respond to these challenges effectively. The lack of trainings on climate change impact on health, insufficient resources and lack of adequate interventions limit their capacity to effectively manage climate health risks. Gaps in knowledge and perceptions related to climate change impacts on health among Sindh Integrated Emergency and Health Services staff to inform targeted trainings and climate-sensitive responsive health interventions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim of the Study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to assess knowledge and measure the perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services (SIEHS).\u0026nbsp;It specifically focuses on frontline health and emergency worker as well as the administrative staff, examining their roles in identifying and responding to climate-related health diseases as well as evaluating the institution\u0026apos;s preparedness of SIEHS to address health risks associated with climate change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhat is the Knowledge and Perceptions of Climate Change Impacts on Health among staff of Sindh Integrated Emergency and Health Services in Karachi, Pakistan?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Objectives:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eTo assess knowledge and perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eOperational Definitions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClimate Change:\u0026nbsp;\u003c/strong\u003eClimate change refers to significant and lasting changes in the Earth\u0026apos;s temperatures and weather patterns. These changes can be from natural causes (such as large volcanic activity) or from human activities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth Illness:\u0026nbsp;\u003c/strong\u003eA state of complete physical, mental, and social well-being and not merely the absence of disease, or infirmity. (WHO 1946).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmergency Respondents:\u0026nbsp;\u003c/strong\u003eAccording to the World Health Organization, Emergency respondents are highly skilled teams to support their communities\u0026rsquo; immediate needs. It includes Emergency Medical Teams (EMTs), composed of doctors, paramedics, and logistical experts, are crucial responders in crises.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePreparedness:\u0026nbsp;\u003c/strong\u003eThe knowledge and capacities developed by governments, response and recovery organizations, communities, and individuals to effectively anticipate, respond to, and recover from the impacts of likely, imminent, or current disasters.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003eAccording to the occupational health literature, climate change adversely impacts the health of workers by exposing them to extreme heat. The highest risk of heat-related illness and mortality is reported to be in workers in agriculture, forestry, fishing, hunting, and construction, which highlights that there is awareness of the risks associated with climate in some occupational groups, but that due to exposure, they are vulnerable. (Nambi Ndugga, 2023)\u003c/p\u003e\n\u003cp\u003eResearch on healthcare providers demonstrates that the majority of them acknowledge climate change as a risk to patient health and are interested in participating in the corresponding discussions. But one of the perceived barriers is perceived lack of time, competing priorities, and limited knowledge, which hinder regular communication with patients, which suggests an awareness-practical disjunction. (Tess Wiskel, 2024)\u003c/p\u003e\n\u003cp\u003eStudies carried out on medical and health sciences students reveal high awareness and little knowledge of climate change and its effects on health. Information is mostly obtained through the internet and social media; less usage of scientific journals and formal curricula is mentioned. Though students believe that climate change is a future challenge to the health of patients, a lack of adequate exposure in the curriculum reduces their readiness to deal with its effects in clinical environments. Research into environmental sustainability knowledge indicates that awareness is moderately high, but there is low understanding, and no stable behaviour is taken in terms of sustainability. (J Irlam, 2023)\u003c/p\u003e\n\u003cp\u003eThe students are aware of the sustainability concept and are not clear about how it applies to health care, which is a weakness in health curricula concerning environmental health and sustainability. According to population-based studies conducted in Canada, there is concern regarding the health effects of climate change, but it depends on the gender, education, and political orientation. Selective perceptions regarding the health risks were expressed by the respondents who were more interested in water, food, and air quality than mental health or infectious diseases (Nora Casson, 2023)\u003c/p\u003e\n\u003cp\u003eOn the same note, a German population survey revealed that there was high recognition of climate change and its health, yet people believe that other people are more affected than they are, thereby displaying cognitive distancing and that there is a need to better communicate risks (Katharina van Baal, 2023)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The study, conducted in UK, showed that people who had personal or local experience with flooding and air pollution had reported far greater concern and more negative views of health effects of climate phenomenon, which implies that the experience of living environment has a strong influence on the perception and awareness. (Hilary Graham, 2022).\u003c/p\u003e\n\u003cp\u003eA study conducted in rural Nigeria showed that there is poor knowledge and good attitude to climate change and thus the need to engage in persistent health promotion and dissemination of information to vulnerable communities (Esther O Asekun-Olarinmoye, 2014)\u003c/p\u003e\n\u003cp\u003eSimilarly, research conducted in Lusaka Province found that there are significant gaps in stakeholder knowledge, especially concerning indirect health effects. Even though the importance of health-related risks posed by climate change is acknowledged, the need to conduct research and education at a local level is evident. (Mwale, 2025)\u003c/p\u003e\n\u003cp\u003eLastly, local public health departments in the United States were assessed and either did not possess expertise, staffing and funds or harboured a low institutional priority regarding climate change because of its perceived as an increasing public health issue. This is indicative of structural knowledge and capacity void in the systems of public health.(Edward W. Maibach 2008)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Framework:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conceptual framework was adapted from published climate change and health impact models (e.g., NSW Health framework), with modifications made to suit the objectives of the present study.\u003c/p\u003e\n\u003cp\u003eThis conceptual frame is adapted and modified from existing climate change and health models that briefly explain the conceptual route through which climate-related health illnesses are caused. The original framework showcases direct and indirect climatic exposures leading to mental and physical health outcomes, with vulnerability acting as a modifying factor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to incorporate this framework, according to this study, the framework has been modified to be consistent with the research objectives and target population. \u0026nbsp;The adapted model highlights knowledge and perceptions of climate change impacts on health. This study focuses on climatic hazards, including extreme weather events such as heatwaves, pollution, flood all of these hazards are considered as health-related exposures, the outcome of interest includes staff perceptions related to it.\u003c/p\u003e\n\u003cp\u003eSociodemographic and professional characteristics, including education, role within the Sindh Integrated Emergency and Health Services, years of experience, and prior training, are considered contextual factors that may influence knowledge and perceptions.\u003c/p\u003e\n\u003cp\u003eThis adapted framework provides a structured approach to understanding how SIEHS staff perceive the link between climate change and health, while remaining within the scope of a cross-sectional assessment of knowledge and perception\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eStudy Design:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Cross-sectional study design was used in this study to assess knowledge and measure perceptions of climate change impacts on health among staff of Sindh Integrated Emergency and Health Services. The data was collected in two phases. The data was collected using a structured and modified questionnaire named the Knowledge, Attitudes, and Practices (KAP) survey, adapted from the UNDP climate change awareness survey. A cross-sectional approach helped to collect the data at a single point in time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out at two major headquarters of Sindh Integrated Emergency and Health Services (SIEHS) in Karachi, Pakistan. Rescue 1122 Headquarters, Gulshan-e-Iqbal was the first site for data collection comprising of two key departments, Emergency Medical Department and Communication and Command Control Centre (CCC), while the second site is located at Central office, PECHS block 6, Siehs office, where the department of Teletabeeb, Research Development and Education (RDE) and Monitoring, Evaluation, Accountability and Learning (MEAL) departments are stationed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population comprised of Staff working across various departments of Sindh Integrated Emergency and Health Services. These consist of the Emergency Medical Department (Pre-hospital and ambulance care), the communication and command and control centre (emergency call handling and dispatch coordination), and the Research, Development and Education (RDE) Department (Performance monitoring and quality assurance). The accessible population comprised all staff members currently stationed in Karachi and those who were present at the time of data collection and provided informed consent. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEmployee currently stationed at any department of Sindh Integrated Emergency and Health Services in Karachi.\u003c/li\u003e\n \u003cli\u003eBelong to any unit of Sindh Integrated Emergency and Health Services.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHave at least 6 months of job experience to ensure exposure to emergency and rescue operations.\u003c/li\u003e\n \u003cli\u003eWilling to provide informed consent and available during the study period.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStaff on leave or temporarily absent during data collection.\u003c/li\u003e\n \u003cli\u003eRetired, resigned, or transferred employees no longer associated with SIEHS.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStaff from other cities or regions outside Karachi.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuantitative data were collected using convenience sampling. This approach was employed because the questionnaire was disseminated through office channels, and participants who responded voluntarily were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling Technique\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Mixed approach (Census + Random sampling) is used to ensure differences in department sizes. Due to the unequal size of departments, a mixed sampling approach was carried out. All staff from smaller departments (\u0026le;10 members) were included (census), while participants from larger departments (\u0026gt;10 members) were selected using simple random sampling to ensure representativeness and feasibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sample size for this study was initially calculated using a single-population proportion formula with a 95% confidence level, 5% margin of error, an assumed proportion of 50%, and a 10% anticipated non-response rate. Although the calculated minimum sample was larger, practical constraints during field data collection limited recruitment. Ultimately, 218 participants were successfully enrolled and included in the final analysis. Prior to the main data collection, a pilot study involving 10 participants was conducted to assess the clarity, reliability, and feasibility of the study instrument. Data from the pilot testing were excluded from the final analysis. All eligible participants who provided informed consent during the study period were included\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment Procedure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The recruitment process was initiated after approval from the Ethical Review Committee (ERC) and Institutional Review Board (IRB) of the Shaheed Zulfiqar Ali Bhutto University of science and Technology (SZABIST). The data was collected between October 2025 and December 2025. \u0026nbsp;Participants were recruited using a nonprobability convenience sampling technique. Eligible participants were staff members of the Sindh Integrated Emergency (SIEHS) working in Karachi. Data were collected at a single point in time through an online survey. The survey link was disseminated through official institutional channels and WhatsApp groups following administrative approval. All participants provided electronic informed consent before participation. A subset of respondents from the same sample voluntarily participated in the qualitative component of the study. Participation was entirely voluntary, and confidentiality and anonymity were ensured.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA self-administered and modified questionnaire comprising 30 closed-ended questions was developed in English and Urdu and then pretested among 12 participants for non-selected staff for assessing content validity, appropriateness, and comprehensibility of the questions. The data was collected through an online Google form. The edited questionnaire was administered by the principal investigator to the 218 participating staff at two different headquarters of Sindh Integrated Emergency and Health Services from December 1st to January 1st. The questionnaire consists of five sections, including socio-demographic information, general knowledge and awareness, experience \u0026amp; observations, perceptions of risk \u0026amp; priorities, health impacts of climate change and training, guidelines \u0026amp; institutional readiness.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Tools \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Data Collection Tool\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantitative data was collected through a structured, validated questionnaire known as the Knowledge, Attitudes, and Practices (KAP) survey adapted from the UNDP climate change awareness survey, to assess knowledge and measure climate change impacts on health. The five-point Likert scale was used, consisting of 30 closed ended questions covering five sections including socio-demographic information, general knowledge and awareness, experience \u0026amp; observations, perceptions of risk \u0026amp; priorities, health impacts of climate change and training, guidelines \u0026amp; institutional readiness.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThese sections aim to assess the knowledge regarding climate change impacts on health among SIEHS staff. Evaluation for positive statements: (1) Strongly disagree, (2) Disagree, (3) Neither (4) Agree, (5) Strongly agree, while evaluation for negative statements: (5) Strongly disagree, (4) Disagree, (3) Neither (2) Agree, (1) Strongly agree were used.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTool Validity:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Knowledge, Attitudes, and Practices (KAP) survey is a validated tool based on the UNDP climate change awareness survey. The UNDP KAP Survey on Climate change awareness developed the tool\u0026rsquo;s content and construct validity through expert review. However, it has not been previously applied in Pakistan; it provided a useful structural framework for developing our questionnaire on climate change knowledge and perceptions. The tool was adaptively modified to reflect local climate change issues in Pakistan. In this study, the instrument was carefully reviewed by the principal investigator for the content relevance and clarity of the language of the tool. This study involving human participants was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch2\u003e\u003cstrong\u003eTable1: Participant\u0026rsquo;s Demographic Characteristics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe study sample comprised a total of 218 participants for the quantitative phase, as shown in Table 1. Out of 218 participants, 139(63.8%) were females, and 79(26.2%) were males. The age distribution reported that 73 (33.49 %) participants were between 18 and 27, 107 (49.08 %) were between 28 and 37 years of age, 32 (14.68 %) were between 38 and 47 years, and 6 (2.75 %) were above 48 years and above of age. The majority of participants were from the Emergency Medical Department, accounting for 110 (50.46 %), 96 (44.04%) were from the Communication and Command and Control Centre, and 34 (11.3%) were from Monitoring, Evaluation, Accountability and Learning (MEAL), followed by 10 (4.59%) and 2 (0.92%) Teletabeeb and Research, Development, and Education Department (RDE), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe maximum number of participants, 86 (39.45%), had a bachelor\u0026apos;s level of education. 47 (21.56%) held a diploma. Respondents with an MS/MPhil included 10 (4.59%). 24 (11.01%) had a secondary level of education. Participants with a primary level of education included 51 (23.39%).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding years of experience, 39 (17.89%) have less than 1 year, 95 (43.58%) have 1\u0026ndash;3 years, 45 (20.64%) have 4\u0026ndash;6 years, and 39 (17.89%) have more than 6 years in the Sindh Integrated Emergency and Health Services.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 1Participant\u0026rsquo;s Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"636\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency\u0026nbsp;(Percentage) n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e18\u0026ndash;27 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e73 (33.49 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e28\u0026ndash;37 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e107 (49.08 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 38\u0026ndash;47 years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e32 (14.68 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e48\u0026ndash;57 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e6 (2.75 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e139(63.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e79 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepartment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eEmergency Medical Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e110 (50.46 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eCommunication and Command and Control Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e96 (44.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eResearch, Development, and Education Department (RDE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e2 (0.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eTeletabeeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e10 (4.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eMonitoring, Evaluation, Accountability, and Learning (MEAL)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e34 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003ePrimary\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003cp\u003eMS/MPhil\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e51(23.39%)\u003c/p\u003e\n \u003cp\u003e24 (11.01%)\u003c/p\u003e\n \u003cp\u003e86 (39.45%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDiploma\u003c/p\u003e\n \u003cp\u003ePhD and above\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10 (4.59%)\u003c/p\u003e\n \u003cp\u003e47 (21.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Experience\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e39 (17.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e95 (43.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.7296%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32.5472%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4\u0026ndash;6 years\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;More than 6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 40.7233%;\"\u003e\n \u003cp\u003e45 (20.64%)\u003c/p\u003e\n \u003cp\u003e39 (17.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Participants Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;comparison\u0026nbsp;of\u0026nbsp;UNDP KAP survey on climate change awareness\u0026nbsp;and\u0026nbsp;its\u0026nbsp;sub-scales\u0026nbsp;scores\u0026nbsp;based\u0026nbsp;on\u0026nbsp;the\u0026nbsp;Socio-Demographic characteristics of participants is shown in Tables 1 and 2, respectively.\u0026nbsp;Analysis showed that age (F: 2.69, p=0.04), Department of SIEHS staff (\u003cem\u003eF:\u0026nbsp;\u003c/em\u003e13.48, p=0.000), Educational Level of participants (F:5.41, p=0.0004) and years of experience at Sindh Integrated Emergency and Health Services (F: 3.95, p=0.0091) had a significant impact on knowledge of SIEHS staff.\u003c/p\u003e\n\u003cp\u003eAnova results showed a statistically significant difference in Knowledge scores among different age groups (F: 2.69, p=0.04), showing age as an influencing factor on Knowledge Levels. The score of knowledge regarding climate change impacts on health was higher among those aged between 28 and 37 (12.84\u0026plusmn; 2.95) and 38 and 47 (13\u0026plusmn; 4.09) as compared to other age groups. According to the post-Hoc comparison, participants aged 28-37 and 38-47 scored significantly higher than other groups (p\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA statistically significant difference was observed in the Knowledge scores based on the departments participants belonged to (F: 13.48, p=0.000).\u0026nbsp;According to the results, the knowledge scores related to climate change were high among the Emergency Medical Department (164.36\u0026plusmn;16.20) as compared to other departments. Analysis\u0026nbsp;further\u0026nbsp;revealed\u0026nbsp;a\u0026nbsp;statistically\u0026nbsp;significant difference in the scores of all other departments. A statistical difference\u0026nbsp;in\u0026nbsp;the\u0026nbsp;Communication and Command and Control Centre was\u0026nbsp;significant\u0026nbsp;(165.48\u0026plusmn;9.17), and participants from Research, Development, and Education (158.69\u0026plusmn;15.86) had more knowledge of climate change impacts on health. Similarly, the Teletabeeb department had knowledge scores of (155.87\u0026plusmn;15.77), and the Department of Monitoring, Evaluation, Accountability and Learning (MEAL) had knowledge scores of (162.88\u0026plusmn;18.06).\u003c/p\u003e\n\u003cp\u003eThe mean knowledge score varied significantly across educational levels. Participants with a bachelor\u0026rsquo;s degree demonstrated the highest mean knowledge score (13.42\u0026plusmn;3.27), followed by those with an MS/M.Phil. degree (13.8\u0026plusmn;3.55), participants with a Diploma reported (12.59\u0026plusmn;3.55) mean scores. While participants holding a secondary degree showcased (13.13\u0026plusmn;4.09).\u0026nbsp;The lowest mean score was observed among participants with primary education (12.51\u0026plusmn;3.94). The differences in knowledge scores across educational categories were statistically significant (p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003eThe mean knowledge scores increased with years of professional experience. Participants with more than 6 years of experience reported the highest mean knowledge score (13.67\u0026plusmn; 4.05), followed by those with participants with less than 1 year of experience (13.39\u0026plusmn;2.62), while the lowest mean score was observed among participants with 1-3 years of experience (12.72\u0026plusmn;3.64) and participants with years of experience between 4-6 years (12.76\u0026plusmn;3.84). The findings showed statistically significant results.\u003c/p\u003e\n\u003cp\u003eThe demographic variable gender did not show a statistically significant difference in knowledge scores between male and female participants. In addition, no significant difference was observed in overall knowledge scores related to the impacts of climate change on health across gender categories, suggesting that gender was not significantly associated with variations in knowledge or perceptions (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e \u003cstrong\u003eDistribution of Knowledge Scores Among Participants by Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003eMean (SD) Total\u003c/p\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003e18\u0026ndash;27 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e12.84\u0026plusmn; 2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep=0.04*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u003cem\u003eF: 2.69\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003e28\u0026ndash;37 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.17\u0026plusmn; 3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003e38\u0026ndash;47 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13\u0026plusmn; 4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003e48\u0026ndash;57 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e12.5 \u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.00\u0026plusmn;3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep=0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003et: \u0026minus;0.1586\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.18\u0026plusmn;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eDepartment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eEmergency Medical Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e164.36\u0026plusmn;16.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep=0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u003cem\u003eF: 13.48\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eCommunication and Command and Control Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e165.48\u0026plusmn;9.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eResearch, Development, and Education Department (RDE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e158.69\u0026plusmn;15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eTeletabeeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e155.87\u0026plusmn;15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eMonitoring, Evaluation, Accountability, and Learning (MEAL)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e162.88\u0026plusmn;18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eEducation Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e12.51\u0026plusmn;3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep= 0.0004*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u003cem\u003eF: 5.41\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.13\u0026plusmn;4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003cp\u003eMS/MPHIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.42\u0026plusmn;3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eDiploma \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e12.59\u0026plusmn;3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6887%;\"\u003e\n \u003cp\u003eYears of Experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8411%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\n \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e\n \u003cp\u003eMore than 6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3576%;\"\u003e\n \u003cp\u003e13.39\u0026plusmn;2.62\u003c/p\u003e\n \u003cp\u003e12.72\u0026plusmn;3.64\u003c/p\u003e\n \u003cp\u003e12.76\u0026plusmn;3.84\u003c/p\u003e\n \u003cp\u003e13.67\u0026plusmn; 4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.3444%;\"\u003e\n \u003cp\u003ep=0.0091*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.76821%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u003cem\u003eF: 3.95\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable3: Distribution of Perceptions Scores Among Participants by Demographic Characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean perception scores differed across participants\u0026rsquo; demographic and professional characteristics (Table X). Age was significantly associated with perception scores (F = 2.82, p = 0.03), with the highest scores observed among participants aged 18\u0026ndash;27 years (9.07\u0026plusmn;2.47) and the lowest among those aged 48\u0026ndash;57 years (7.00\u0026plusmn;3.83).\u003c/p\u003e\n\u003cp\u003eSignificant differences were found in perception scores across departments (F = 13.48, p \u0026lt; 0.001). Participants from the Emergency Medical Department had the highest mean score (9.03\u0026plusmn;2.33), while lower scores were observed in other departments. (Note: Some department data appear inconsistent and may require verification).\u003c/p\u003e\n\u003cp\u003eEducation level also showed a significant association with perception scores (F = 7.27, p \u0026lt; 0.001). Participants with Bachelor\u0026rsquo;s (9.21\u0026plusmn;2.20) and MS/MPhil degrees (8.40\u0026plusmn;2.07) had higher scores compared to those with Primary (7.71\u0026plusmn;2.58) or Secondary education (6.71\u0026plusmn;2.16). Diploma holders scored 8.89\u0026plusmn;2.47.\u003c/p\u003e\n\u003cp\u003eFinally, years of experience were significantly associated with perception scores (F = 3.70, p = 0.01). Participants with less than 1 year of experience had the highest scores (9.62\u0026plusmn;2.14), followed by those with more than 6 years (8.49\u0026plusmn;2.52), 4\u0026ndash;6 years (8.27\u0026plusmn;2.28), and 1\u0026ndash;3 years of experience (8.11\u0026plusmn;2.57). These results indicate that age, department, education level, and years of experience significantly influence perception scores among participants, whereas gender does not.\u003c/p\u003e\n\u003cp\u003eGender was not significantly associated with perception scores (t = \u0026minus;0.9686, p = 0.333); males had a mean score of 8.43\u0026plusmn;2.51, while females scored 9.18\u0026plusmn;1.66.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDistribution of Perception Scores Among Participants by Demographic Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;(SD) Total\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGDPBS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e18\u0026ndash;27 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e9.07\u0026plusmn;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep=0.03*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF:\u0026nbsp;2.82\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e28\u0026ndash;37 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e8.32\u0026plusmn;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e38\u0026ndash;47 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e7.94\u0026plusmn;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e48\u0026ndash;57 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026nbsp;7\u0026plusmn; 3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e8.43\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003ep=0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003et:\u0026nbsp;-0.9686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e9.18\u0026plusmn;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepartment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eEmergency Medical Department\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e9.03\u0026plusmn;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep=0.000*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF:\u0026nbsp;13.48\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eCommunication and Command and Control Centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026nbsp; 7.68\u0026plusmn;2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eResearch, Development, and Education Department (RDE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026plusmn;15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eTeletabeeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e155.87\u0026plusmn;15.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eMonitoring, Evaluation, Accountability, and Learning (MEAL)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e162.88\u0026plusmn;18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e7.71\u0026plusmn;2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep= 0.000*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u003cem\u003eF:\u0026nbsp;7.27\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026nbsp;6.71\u0026plusmn; 2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eBachelors\u003c/p\u003e\n \u003cp\u003eMS/MPHIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e9.21\u0026plusmn;2.20\u003c/p\u003e\n \u003cp\u003e8.4\u0026plusmn;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eDiploma \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e8.89\u0026plusmn;2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8185%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYears of Experience\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.7852%;\"\u003e\n \u003cp\u003eLess than 1 year\u003c/p\u003e\n \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e\n \u003cp\u003e4\u0026ndash;6 years\u003c/p\u003e\n \u003cp\u003eMore than 6 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5159%;\"\u003e\n \u003cp\u003e\u0026nbsp;9.62\u0026plusmn; 2.14\u003c/p\u003e\n \u003cp\u003e8.11\u0026plusmn; 2.57\u003c/p\u003e\n \u003cp\u003e8.27\u0026plusmn;2.28\u003c/p\u003e\n \u003cp\u003e8.49\u0026plusmn; 2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.3313%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP= 0.01*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.5492%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u003cem\u003eF: 3.70 \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Correlation between knowledge and perception scores regarding climate change impacts on health\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpearman\u0026rsquo;s correlation analysis showed a \u003cstrong\u003estatistically significant weak positive relationship\u003c/strong\u003e between knowledge and perception scores regarding climate change impacts on health (r = 0.195, p = 0.004), based on responses from 218 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCorrelation between knowledge and perception scores regarding climate change impacts on health\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0937%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8708%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003er\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.0937%;\"\u003e\n \u003cp\u003eKnowledge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.8708%;\"\u003e\n \u003cp\u003ePerceptions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.9321%;\"\u003e\n \u003cp\u003er=0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.5477%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep=\u003c/strong\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003cstrong\u003e*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.5557%;\"\u003e\n \u003cp\u003eN=218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Strengths\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis\u0026nbsp;study\u0026nbsp;has\u0026nbsp;several\u0026nbsp;strengths.\u0026nbsp;Firstly,\u0026nbsp;to\u0026nbsp;our\u0026nbsp;knowledge,\u0026nbsp;this\u0026nbsp;study\u0026nbsp;is\u0026nbsp;one\u0026nbsp;of\u0026nbsp;the\u0026nbsp;first quantitative studies\u0026nbsp;conducted\u0026nbsp;among clinical and non-clinical staff,\u0026nbsp;aiming\u0026nbsp;to\u0026nbsp;assess\u0026nbsp;the knowledge and perceptions regarding Climate Change impacts of health among Sindh Integrated Emergency and Health Services staff,\u0026nbsp;which\u0026nbsp;is\u0026nbsp;an\u0026nbsp;emerging\u0026nbsp;topic\u0026nbsp;to\u0026nbsp;address the knowledge and perceptions\u0026nbsp;gap\u0026nbsp;among front-line professionals\u0026nbsp;in\u0026nbsp;high-risk\u0026nbsp;areas\u0026nbsp;of\u0026nbsp;Karachi,\u0026nbsp;Pakistan.\u0026nbsp;The\u0026nbsp;quantitative\u0026nbsp;data allowed us to understand the level of Knowledge and perceptions among SIEHS staff.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondly, the participation of diverse staff from different departments of Sindh Integrated Emergency and Health Services enhanced the generalizability. The findings from the study could serve as a potential implication and guide for policymakers, particularly in the healthcare and education sectors, can use the findings from this study to design and implement targeted interventions that improve the knowledge, preparedness, and response of SIEHS staff regarding the health impacts of climate change, ultimately contributing to the physical, mental, and social well-being of the communities they serve..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some limitations. Its cross-sectional design prevents establishing causal relationships, and data were self-reported, which may be affected by recall or social desirability bias. The sample was limited to SIEHS staff, which may reduce generalizability to other regions or emergency services. Additionally, qualitative insights were not collected, which could have provided a deeper understanding of staff perceptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt is recommended that targeted training programs be implemented to improve SIEHS staff knowledge of climate change and its health impacts. Policies should integrate climate-related health risks into emergency response planning, and future research should include qualitative and longitudinal studies to better understand staff perceptions and preparedness. Expanding the study to other regions would also enhance generalizability.\u003c/p\u003e\n\u003cp\u003eHere is a short, high-quality, journal-ready Discussion section (concise, integrated, scholarly, and literature-linked) suitable for manuscripts with word limits. It maintains academic rigor, compares findings with other studies, and is formatted as one coherent section.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides one of the first quantitative assessments of knowledge and perceptions regarding the health impacts of climate change among both clinical and non-clinical staff of Sindh Integrated Emergency and Health Services (SIEHS) in Karachi, Pakistan. Conducted in a highly climate-vulnerable urban context, the study addresses a critical research gap by examining climate\u0026ndash;health awareness among frontline emergency and health professionals who play a central role in disaster response and population health protection.\u003c/p\u003e\n\u003cp\u003eThe findings indicate that SIEHS staff possess moderate overall knowledge and perceptions of climate change and its health consequences, with awareness of key risks such as heat-related illnesses, respiratory diseases, vector- and water-borne infections, and psychological distress during disasters. These results are consistent with international studies among healthcare professionals in high-income and low- and middle-income countries, which similarly report moderate to high climate\u0026ndash;health awareness but uneven preparedness and institutional integration (Kotcher et al., 2021; Wiskel et al., 2024; Caraballo-Betancort et al., 2025; Hira Tariq et al., 2025).\u003c/p\u003e\n\u003cp\u003eSignificant differences in knowledge and perception scores across age, department, education level, and years of experience reflect structural and professional inequalities in access to climate\u0026ndash;health exposure and training. Higher knowledge among Emergency Medical Department and Communication and Command and Control Centre staff aligns with evidence that frontline exposure to climate-related emergencies strengthens climate\u0026ndash;health understanding (Kang et al., 2023; Theron et al., 2025). Similarly, the association between higher education and greater knowledge supports findings from China and South Africa, where formal education enhances climate literacy but does not ensure operational preparedness (Yang et al., 2018; Irlam et al., 2023).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHigher perception scores among younger staff and frontline emergency workers mirror international research showing that direct experience with environmental hazards and generational exposure to climate discourse increase perceived health risks (Graham et al., 2022; Casson et al., 2023; van Baal et al., 2023). The higher perception among newly employed staff likely reflects recent academic exposure to climate change content, consistent with findings from studies among health students and early-career professionals (Yang et al., 2018; Irlam et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe weak but significant correlation between knowledge and perception highlights a critical knowledge\u0026ndash;perception gap, indicating that increased awareness does not necessarily translate into stronger risk perception or preparedness. This pattern is widely documented in climate\u0026ndash;health literature and underscores the limitations of information-based training alone (Maibach et al., 2008; Kotcher et al., 2021; Wiskel et al., 2024).\u003c/p\u003e\n\u003cp\u003eOverall, these findings emphasize the need for structured climate\u0026ndash;health training, experiential learning, and institutional integration of climate resilience frameworks within emergency health systems. Strengthening intersectoral collaboration between healthcare services, disaster management authorities, and environmental agencies is essential for building climate-resilient health systems and protecting vulnerable urban populations. Despite being limited to a single institution, this study provides critical baseline evidence for Pakistan and contributes to the limited literature on climate\u0026ndash;health awareness in emergency service systems in low- and middle-income countries.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights that SIEHS staff possess varying levels of knowledge and perceptions regarding the health impacts of climate change, with notable gaps in awareness and preparedness. Strengthening staff capacity through targeted training, integrating climate-related health risks into emergency response protocols, and fostering intersectoral collaboration are essential to enhance resilience and ensure effective community health protection. Addressing these gaps will not only improve the readiness of emergency health services but also contribute to safeguarding the physical, mental, and social well-being of the populations they serve in the face of climate-related challenges.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSZABIST-\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eShaheed Zulfiqar Ali Bhutto University of Science and Technology\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSIEHS- Sindh Integrated Emergency and Health Services\u003c/p\u003e\n\u003cp\u003eGHGs - Greenhouse gases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCC- Climate Change\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLMICs- Low and Middle-Income Countries\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNDMA- National Disaster Management Authority\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWHO- World Health Organization\u003c/p\u003e\n\u003cp\u003eEMTs- Emergency Medical Teams\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCC- Communication and Command Control Centre\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRDE- Research Development and Education\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMEAL- Monitoring, Evaluation, Accountability and Learning\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKAP- Knowledge, Attitudes, and Practices\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUNDP- United Nations Development Programme\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Ethical Review Committee and Institutional Review Board of Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (SZABIST), Karachi, Pakistan.\u003cbr\u003e\u0026nbsp;All participants provided electronic informed consent prior to participation.\u003cbr\u003e\u0026nbsp;The study was conducted in accordance with the Declaration of Helsinki and relevant institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShehzeen Niaz conceived the study, developed the methodology, conducted analysis and drafted the manuscript. Rabiya Javed contributed to data collection and manuscript preparation. Suleman Otho supervised the study and critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of Sindh Integrated Emergency and Health Services, Karachi, for their cooperation and participation in this study. We also acknowledge that the questionnaire used in this research was adapted and modified from the Climate Change Knowledge, Attitudes and Practices (KAP) survey developed under the climate change awareness campaign supported by the United Nations Development Programme (UNDP) (Fontenard T., Grenada Climate Change Awareness Survey Report). The original report is publicly available online and served as a valuable reference in developing the study instrument.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAntonio Miguel Caraballo-Betancort, I. M.-T., Blanca Notario-Pacheco, Shkelzen Cekrezi, Ana Perez-Moreno, Maria Martinez-Andres (2025). Health professionals\u0026apos; perceptions of climate change: A systematic review of qualitative studies. \u003cem\u003ePublic Health\u003c/em\u003e,\u003cem\u003e 245\u003c/em\u003e, 105773. https://doi.org/https://doi.org/10.1016/j.puhe.2025.105773 \u003c/p\u003e\n\u003cp\u003eAyesha Fahim, H. A. Q., Ahsan Sethi. (2025). 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(2022). \u003cem\u003ePakistan: Floods - Jul 2022\u003c/em\u003e. https://reliefweb.int/disaster/fl-2022-000254-pak\u003c/p\u003e\n\u003cp\u003ereliefweb. (2024). \u003cem\u003ePakistan: Floods - Feb 2024\u003c/em\u003e. https://reliefweb.int/disaster/fl-2024-000020-pak\u003c/p\u003e\n\u003cp\u003eTess Wiskel, T. T. M., Mariel Fonteyn, Kristin Stevens, Chelsea Heberlein, Nathaniel Matthews-Trigg, Caleb Dresser \u0026amp; Aaron Bernstein (2024). Frontline clinic perspectives on climate change, human health, and resilience: a national cross-sectional survey.\u003cem\u003e BMC Primary Care 25\u003c/em\u003e(2024). https://doi.org/https://doi.org/10.1186/s12875-024-02622-y \u003c/p\u003e\n\u003cp\u003eURAANpakistan. (2024). \u003cem\u003eENVIRONMENT \u0026amp; CLIMATE CHANGE\u003c/em\u003e. https://uraanpakistan.pk/climate-change/\u003c/p\u003e\n\u003cp\u003eWHO. (2023). \u003cem\u003eClimate Change\u003c/em\u003e. https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health\u003c/p\u003e\n"}],"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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate change, Health impacts, Knowledge and perceptions, Emergency health services","lastPublishedDoi":"10.21203/rs.3.rs-8887385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8887385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives:\u003c/h2\u003e \u003cp\u003eTo assess knowledge and perceptions of climate change impacts on health among clinical and non-clinical staff of the Sindh Integrated Emergency and Health Services (SIEHS) in Karachi, Pakistan.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted among 218 staff members of Sindh Integrated Emergency and Health Services (SIEHS) using a structured Knowledge, Attitudes, and Practices questionnaire adapted from the United Nations Development Programme climate awareness survey. Data were collected through an online self-administered questionnaire. Descriptive statistics, independent t-tests, ANOVA, and Spearman correlation were applied.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eParticipants demonstrated moderate overall knowledge and perceptions of climate change and its health impacts. Statistically significant differences in knowledge and perception scores were observed across age groups, departments, education levels, and years of experience (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Emergency Medical Department staff and participants with higher education levels showed significantly higher scores. A weak but statistically significant positive correlation was identified between knowledge and perception scores (r\u0026thinsp;=\u0026thinsp;0.195, p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eAlthough SIEHS staff demonstrate moderate awareness of climate-related health risks, gaps remain in perceived preparedness and institutional readiness. Targeted climate-health training and structured preparedness programmes may help strengthen workforce response capacity in climate-vulnerable urban settings.\u003c/p\u003e","manuscriptTitle":"Knowledge And Perception of Climate Change Impacts on Health Among Staff of Sindh Integrated Emergency and Health Services in Karachi, Pakistan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 09:14:31","doi":"10.21203/rs.3.rs-8887385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T06:42:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-11T17:58:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106252129463146507954419398299944845943","date":"2026-03-29T09:22:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T06:22:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T10:46:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50860278721170043482820383463389093154","date":"2026-03-28T09:13:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116101159805262029477174793484506315474","date":"2026-03-28T08:49:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174847868355041794403825328856757978999","date":"2026-03-26T04:52:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T12:45:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T10:19:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T06:01:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T17:04:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-02-23T16:58:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d861350-2bc1-4d41-96e9-ea2bc21493bb","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T06:42:37+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T06:54:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 09:14:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8887385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8887385","identity":"rs-8887385","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}