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Knowledge, Attitude and Practices Regarding Health Insurance Among Class IV Hospital Workers: A Cross-Sectional Study
* Corresponding author: Dr. Sonali Kenchanagouda Patil, Department of Hospital Administration, Jawaharlal Nehru Medical College, Nehru Nagar, Belagavi, Karnataka, India. psonali75@gmail.com
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Received: ,
Accepted: ,
How to cite this article: Patil SK, Kour H, Patil A, Chougule S, Jadhav RA. Knowledge, Attitude and Practices Regarding Health Insurance Among Class IV Hospital Workers: A Cross-Sectional Study. J Health Allied Sci NU. doi: 10.25259/JHASNU_166_2025
Abstract
Objectives
Class IV hospital workers are critical to healthcare delivery but often face limited health insurance awareness, impacting their financial security. To assess knowledge, attitude, and practices (KAP) regarding health insurance among Class IV hospital workers and examine associations with sociodemographic factors.
Material and Methods
A cross-sectional study was conducted from September 2024 to February 2025 among 74 Class IV workers in a tertiary care hospital in Belagavi, India. Participants were selected through convenience sampling. Data were collected using a pre-validated, structured questionnaire comprising 10 items on knowledge, 9 on attitudes, and 10 on practices. Internal consistency was confirmed with Cronbach’s alpha >0.70 for each domain. Scores were categorised as “good,” “moderate,” or “poor” using percentile distribution. Statistical analysis included descriptive statistics and Chi-square tests. p <0.05 was considered significant.
Results
Of the participants, 57% were female. While 67.6% strongly agreed that health insurance is essential, 63.5% were unaware of specific benefits available to them. Significant associations were found between knowledge and age (p = 0.043), income (p = 0.003), and years of service (p = 0.004). Practice levels were significantly associated with income (p = 0.007). No significant associations were found between attitude scores and any sociodemographic variable.
Conclusion
Most workers possess moderate insurance knowledge with significant income-based disparities. Interventions should address the specific needs of the moderate knowledge group through targeted education and financial support.
Keywords
Attitude
Class IV hospital workers
Cross-sectional study
Health insurance
Knowledge
Practice
INTRODUCTION
Universal health coverage (UHC) remains a fundamental goal in public health, aimed at ensuring access to quality healthcare services without financial hardship. One of the key pillars of UHC is health insurance, which plays a critical role in mitigating out-of-pocket healthcare expenses and improving access to timely medical interventions.[1] In low- and middle-income countries (LMICs), such as India, where a large proportion of the population is employed in the unorganised and semi-organised sectors, the availability and awareness of health insurance remain uneven. Among these vulnerable groups, Class IV hospital workers represent a particularly underserved demographic.
Class IV hospital workers include support staff such as ward attendants, sanitation workers, lift operators, and other auxiliary personnel who are instrumental in maintaining the functioning of hospitals. Despite their integral role, these workers often operate under precarious working conditions, low income, and limited job security. They face numerous occupational hazards, including exposure to infections, physical strain, and irregular work hours, all of which underscore the need for adequate health protection mechanisms, such as insurance coverage.[2]
In India, although initiatives such as Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (PM-JAY) aim to expand insurance coverage, awareness and uptake among lower-income groups remain suboptimal.[3] National surveys, including the National Family Health Survey (NFHS-4), reveal that only 28.7% of households had at least one member covered by a health insurance policy.[4] Furthermore, data from the National Sample Survey Office (NSSO) in 2014 showed that >80% of Indians still lacked any form of health insurance coverage, reflecting persistent gaps in policy reach and health literacy.[4]
Numerous studies have highlighted that knowledge, attitude, and practices (KAP) regarding health insurance directly influence its uptake and utilisation.[5-9] However, there is limited literature specifically targeting Class IV hospital workers in this context. Understanding how this group perceives, understands, and uses health insurance is crucial to designing targeted interventions that promote equitable access to healthcare.
This study was undertaken to bridge this knowledge gap by assessing the KAP related to health insurance among Class IV hospital workers in a tertiary care hospital in Belagavi, Karnataka. By identifying the socio-demographic factors associated with insurance awareness and behaviour, the study aims to contribute to evidence-based strategies that enhance financial protection for this overlooked segment of the healthcare workforce.
MATERIAL AND METHODS
A descriptive cross-sectional study was conducted among Class IV workers at a tertiary care teaching hospital in Belagavi, Karnataka. The study was carried out over 6 months, from September 2024 to February 2025.
A convenience sampling technique was used to select 70 participants who met the inclusion criteria, which comprised currently employed Class IV workers at the hospital. Workers who did not consent to participate were excluded. The sample size was calculated based on a 95% confidence level, an estimated proportion of 60%, and a 20% allowable error. This resulted in a required sample size of 70 participants.[6] The sample size was calculated using the formula for estimating a single proportion in a cross-sectional study:
n = (Z2*p*q)/d2 Where, Z = 1.96 (for a 95% confidence level), p = Expected proportion of participants with good knowledge (0.60, based on prior literature, q = 1 - p (0.40), and d = Desired precision (set at 20% of p, i.e., 0.20 * 0.60 = 0.12).
This calculation yielded a minimum sample size of 64. To accommodate potential non-response, the target sample size was set at 70. A total of 78 potential participants were approached, of which 74 agreed to participate and completed the questionnaire, yielding a response rate of 94.9%. Therefore, the final analysis included 74 participants.
Data collection procedure
Data collection was conducted over the study period by the principal investigator. Potential participants were approached in person during their break hours at the hospital. The study’s purpose, procedures, and confidentiality were explained, and written informed consent was obtained.
Data were collected using a self-administered, paper-based questionnaire. To account for potential variations in literacy, the investigator was present to provide assistance and clarify doubts, ensuring accurate and complete responses.
The questionnaire was translated into the local language, Kannada, and back-translated into English to ensure conceptual equivalence. Participants were offered the choice to complete the form in either Kannada or English. A total of 78 potential participants were approached. Of these, 74 agreed and completed the questionnaire, yielding a high response rate of 94.9%. These 74 respondents constituted the final sample for data analysis.
Tool development and validation
The survey instrument was a structured questionnaire developed through a comprehensive literature review on health insurance literacy.[7,8] It comprised three sections: Knowledge (10 items with dichotomous and multiple-choice formats), Attitude (9 items measured on a 5-point Likert scale from Strongly Disagree to Strongly Agree), and Practice (10 items measured on a 5-point Likert scale from Never to Always). Content validity was established by a panel of five experts (two from Public Health, one from Health Economics, one Biostatistician, and one Hospital Administrator), who evaluated relevance and clarity, yielding a Scale-Level Content Validity Index (S-CVI) of 0.91. The questionnaire was translated into Kannada and back-translated into English. A pilot study was conducted with five participants to assess comprehension, wording, and logistical feasibility, rather than for psychometric testing [cite a methodology reference if available, e.g., “Polit & Beck”]. Based on pilot feedback, minor revisions were made, including simplifying the term “health insurance scheme” to “plan” and adding examples for “network hospitals.” Internal consistency was high, with Cronbach’s alpha = 0.75 (Knowledge), 0.84 (Attitude), and 0.81 (Practice). For a more nuanced analysis, total scores were categorised into three levels: Poor, Moderate, and Good, based on the percentile distribution (≤33rd, 34th-66th, ≥67th percentiles) within our study population, a method used in similar KAP studies.[8-12]
Scoring and categorisation
The scoring system was designed based on the structure of the questionnaire. The Knowledge section (10 items) used dichotomous and multiple-choice formats. Each correct answer was awarded 1 point, and an incorrect or ‘don’t know’ response received 0 points. The total score ranged from 0-10. The Attitude section (9 items) was rated on a 5-point Likert scale from 1 (Strongly Disagree) to 5 (Strongly Agree), with a total score range of 9-45. The Practice section (10 items) was rated on a 5-point Likert scale from 1 (Never) to 5 (Always), with a total score range of 10-50.
To enable a nuanced analysis that identifies gradients of understanding and behaviour, the total scores for each domain were categorised into three tiers based on the percentile distribution within our study population, a method endorsed in health research.[13-15] Participants scoring ≤33rd percentile were classified as “Poor,” those scoring between the 34th and 66th percentiles as “Moderate,” and those scoring >66th percentile as “Good.” The resulting score ranges for each category were as follows: for Knowledge, scores of 0-6 were Poor, 7-8 were Moderate, and 9-10 were Good; for Attitude, scores of 9-37 were Poor, 38-41 were Moderate, and 42-45 were Good; and for Practice, scores of 10-32 were Poor, 33-38 were Moderate, and 39-50 were Good.[12-15] Ethical approval was obtained from the Institutional Ethics Committee (Ref No: MDC/JNMCIEC/315, dated 29/04/2024). Written informed consent was secured from all participants in their local language. Anonymity and confidentiality were maintained throughout the study.
Statistical analysis
Data were analysed using SPSS Statistics version 29.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were computed for all variables; categorical data were presented as frequencies and percentages (n,%), while continuous data were summarised as means and standard deviations (Mean ± SD). The composite scores for KAP were calculated and subsequently categorised into three ordinal levels (Poor, Moderate, Good) based on the 33rd and 66th percentile cut-offs within the study population. The associations between these three-tier KAP categories and socio-demographic variables were analysed using the Pearson Chi-Square (χ2) test of independence. For any significant associations identified (p <0.05), the strength of the association was quantified using Cramer’s V statistic. The threshold for interpreting Cramer’s V was as follows: 0.10 = weak effect, 0.30 = moderate effect, and 0.50 = strong effect.
RESULTS
A descriptive cross-sectional study was conducted among 74 Class IV hospital workers. The majority of participants were female (57.0%) and lived in nuclear families (74.3%). The population was predominantly low-income, with 40.5% earning <₹10,000/ month and 52.7% earning between ₹10,000-20,000. Most participants (63.5%) worked <199 h/ month, and 51.4% had ≥10 years of service, indicating an experienced workforce. The detailed socio-demographic profile has been presented in Table 1.
| Characteristic | Category | Frequency (n) | Percentage (%) |
|---|---|---|---|
| Sex | Female | 42 | 57.0 |
| Male | 32 | 43.0 | |
| Family type | Nuclear | 55 | 74.3 |
| Joint | 19 | 25.7 | |
| Monthly income (₹) | <10,000 | 30 | 40.5 |
| 10,000-20,000 | 39 | 52.7 | |
| >20,000 | 5 | 6.8 | |
| Working hours/month | <199 | 47 | 63.5 |
| ≥200 | 27 | 36.5 | |
| Years of service | 0-4 | 15 | 20.3 |
| 5-9 | 21 | 28.4 | |
| ≥10 | 38 | 51.4 |
When composite scores were categorised into three tiers, the distribution of KAP levels revealed significant insights. For Knowledge, only 32.4% of participants demonstrated ‘Good,’ while a substantial 35.1% fell into a ‘Moderate’ category, highlighting a critical gap in detailed understanding. Attitude was more positive, with 67.6% of respondents holding ‘Good’ attitudes towards health insurance. However, Practices were less optimal, with only 33.8% demonstrating ‘Good’ practices and an equal proportion (33.8%) demonstrating ‘Poor’ practices, indicating a clear barrier between positive attitude and actual utilisation. The detailed KAP distributions have been presented in Table 2.
| Domain | Category | Score range | Frequency (n) | Percentage (%) |
|---|---|---|---|---|
| Knowledge | Poor | 0-6 | 24 | 32.4 |
| Moderate | 7-8 | 26 | 35.1 | |
| Good | 9-10 | 24 | 32.4 | |
| Attitude | Poor | 9-37 | 25 | 33.8 |
| Moderate | 38-41 | 24 | 32.4 | |
| Good | 42-45 | 25 | 33.8 | |
| Practice | Poor | 10-32 | 25 | 33.8 |
| Moderate | 33-38 | 24 | 32.4 | |
| Good | 39-50 | 25 | 33.8 |
The analysis using the three-tier KAP categorisation revealed a nuanced pattern of associations [Table 3]. Knowledge was significantly associated with educational status (p = 0.038), years of service (p = 0.004), and income (p = 0.003). Participants with higher education, longer tenure, and higher income were more likely to have better knowledge. Practice was significantly associated only with income (p = 0.007), reinforcing that economic capacity is a primary driver of actual insurance utilisation. No significant associations were found between any socio-demographic variable and attitude scores, confirming that positive perceptions of health insurance are widely held across different demographics within this worker group.
| Socio-demographic Variable | Category | Knowledge (p value) | Attitude (p value) | Practice (p value) |
|---|---|---|---|---|
| Sex | Female/Male | χ2 = 2.45, p = 0.294 | χ2 = 3.82, p = 0.148 | χ2 = 1.04, p = 0.595 |
| Educational status | Below 10th/10th-PU/Grad+ | χ2 = 10.18, p = 0.038* | χ2 = 5.82, p = 0.213 | χ2 = 3.12, p = 0.537 |
| Family type | Nuclear/Joint | χ2 = 0.48, p = 0.785 | χ2 = 2.26, p = 0.323 | χ2 = 2.95, p = 0.229 |
| Monthly income (₹) | <10k/10k-20k/>20k | χ2 = 16.12, p = 0.003* | χ2 = 9.87, p = 0.079 | χ2 = 14.32, p = 0.007* |
| Working hours/month | <199/≥200 | χ2 = 3.81, p = 0.149 | χ2 = 1.64, p = 0.441 | χ2 = 0.55, p = 0.759 |
| Years of service | 0-4/5-9/10+ | χ2 = 15.21, p = 0.004* | χ2 = 4.25, p = 0.373 | χ2 = 5.12, p = 0.275 |
DISCUSSION
This study provides a nuanced assessment of health insurance literacy among Class IV hospital workers, revealing critical disparities between awareness, attitude, and actual practice. The three-tier categorisation of KAP scores uncovered a substantial “moderate knowledge” group (35.1%), highlighting that most workers possess basic awareness but lack the proficiency for confident utilisation, a finding that was obscured in binary analysis.
The strong association between knowledge and both years of service (p = 0.004) and income (p = 0.001) aligns with existing literature. Workers with longer tenure likely benefit from cumulative exposure to workplace information and potentially health events that prompt insurance engagement. This echoes findings by Holst et al., where experience with healthcare systems improved insurance literacy.[11] Similarly, the income-knowledge relationship reflects broader patterns where economic capacity enables access to information and services.[8-15]
Notably, our refined analysis revealed a significant association between knowledge and educational status (p = 0.038), which was not detected in our initial binary model. This suggests that formal education provides crucial foundational skills that help workers progress from “poor” to “moderate” knowledge levels, even if it does not always guarantee “good” knowledge. This nuanced finding aligns with Wang & Lo’s emphasis on education as a cornerstone of health literacy, while explaining why basic education alone may not ensure comprehensive understanding.[16]
Attitudes were overwhelmingly positive, with 87.8% recognising insurance’s financial protective role. The lack of demographic associations suggests these perceptions are shaped more by shared workplace context than individual characteristics. This finding contrasts with observations from studies conducted among healthcare workers in other settings, where demographic factors were found to influence health insurance awareness, possibly due to differences in socioeconomic distribution.[17]
The significant association between income and practice (p = 0.007) underscores how financial constraints remain a primary barrier to utilisation, supporting findings from previous studies highlighting affordability gaps in healthcare access.[18] The universal non-use of online portals (100%) reveals a critical digital divide that threatens to exclude this workforce from increasingly digitalised services under schemes like Ayushman Bharat.[3]
The identification of a substantial “moderate practice” group (32.4%), coupled with the absence of practice associations with education or service years, indicates that structural rather than individual factors determine utilisation. These workers likely understand insurance conceptually but face systemic barriers in execution.
CONCLUSION
This study concludes that health insurance literacy among Class IV hospital workers is a complex, multi-layered issue. The use of a three-tier classification revealed that the majority of workers possess only moderate knowledge and practice levels, a critical finding that was masked by a simple good/poor binary.
Crucially, knowledge was significantly influenced by a combination of educational status, income, age, and years of service, revealing that these factors work together to build a gradient of understanding. In contrast, practice was primarily driven by income, underscoring that financial barriers are the ultimate gatekeeper to utilising benefits, even when knowledge and positive attitudes are present.
These insights move beyond individual blame and highlight a systemic problem. Therefore, interventions must be equally multifaceted. To be effective, they must combine targeted educational programs that address the specific gaps of the “moderate” knowledge group, structural support to simplify bureaucratic processes, and financial protection measures to overcome the cost barriers that prevent this essential workforce from accessing the healthcare security they need.
This study has several limitations. The convenience sampling method may limit generalisability. The cross-sectional design prevents establishing causal relationships. Some potentially relevant variables like digital literacy were not assessed. The single-centre design may not represent all tertiary care settings in India.
Ethical approval
The research/study was approved by the Institutional Review Board at JNMC, number MDC/JNMCIEC/315, dated 29th April 2024.
Declaration of patient consent
The authors certify that they have obtained all appropriate participation consent forms. In the form, the participants have given their consent for their clinical information to be reported in the journal. The participants understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation
The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
References
- Universal health coverage (UHC). Geneva: WHO; 2023. https://www.who.int/health-topics/universal-health-coverage. [Last accessed on 2025 Jan 9]
- Occupational safety and health: key tools and instruments. Geneva: ILO; 2020. https://www.ilo.org/topics-and-sectors/safety-and-health-work. [Last accessed on 2025 Jan 9]
- Ayushman Bharat Pradhan mantra Jan Arogya yojana (PM-JAY). New Delhi: Government of India; 2020. https://pmjay.gov.in. [Last accessed on 2025 Jan 9]
- National family health survey (NFHS-4), 2015-16: India. Mumbai: IIPS; 2017. https://dhsprogram.com/pubs/pdf/fr339/fr339.pdf [Last accessed on 2025 Jul 23]
- Key indicators of social consumption in India: Health. NSS 71st round. New Delhi: Ministry of statistics and programme implementation, Government of India; 2014. https://www.mospi.gov.in/sites/default/files/publication_reports/KI_Health_75th_Final.pdf [Last accessed on 2025 Jul 23]
- Educational intervention to improve health insurance literacy among Class IV hospital workers: a study. J Health Educ Res Dev. 2017;25:78-92.
- [Google Scholar]
- Extending health insurance to the poor in India: An impact evaluation of Rastriya swarthy Bima yojana on out of pocket spending for healthcare. Soc Sci Med. 2017;181:83-92.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- The role of health insurance literacy in the process and outcomes of choosing a health insurance policy in the Netherlands. BMC Health Serv Res. 2023;23:1002.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- The financial burden from non-communicable diseases in low- and middle-income countries: A literature review. Health Res Policy Syst. 2013;11:31.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Health insurance: Awareness, utilization, and its determinants among the urban poor in Delhi, India. J Epidemiol Glob Health. 2018;8:69-76.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- The role of health insurance literacy in the process and outcomes of choosing a health insurance policy in the Netherlands. BMC Health Serv Res. 2023;23:1002.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Content validity index in scale development. J Cent South Univ (Medical Sciences). 2012;37:152-5.
- [Google Scholar]
- Morbidity and utilization of healthcare services among people with cardiometabolic disease in three diverse regions of rural India. Chronic Illn. 2023;19:873-88.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Knowledge, attitudes, and practices towards COVID-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: A quick online cross-sectional survey. Int J Biol Sci. 2020;16:1745-52.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Healthcare finance in the kingdom of Saudi Arabia: A Qualitative study of householders’ attitudes. Appl Health Econ Health Policy. 2018;16:55-64.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Improving patient health literacy in hospitals - A challenge for hospital health education programs. Risk Manag Healthc Policy. 2021;14:4415-24.
- [CrossRef] [PubMed] [PubMed Central] [Google Scholar]
- Determinants of attitudes toward health insurance: Evidence from Brazil. Public Health. 2019;168:1-7.
- [CrossRef] [PubMed] [Google Scholar]
- Catastrophic payments and impoverishment due to out-of-pocket health spending. Econ Polit Wkly. 2011;46:63-70.
- [Google Scholar]