Patient satisfaction and perceived quality of care: Evidence from a cross-sectional national exit survey of HIV and non-HIV service users in Zambia

BMJ Open, Volume 5, No. 12, Year 2015

Interprétation

Objective: To examine the associations between perceived quality of care and patient satisfaction among HIV and non-HIV patients in Zambia. Setting: Patient exit survey conducted at 104 primary, secondary and tertiary health clinics across 16 Zambian districts. Participants: 2789 exiting patients. Primary independent variables: Five dimensions of perceived quality of care (health personnel practice and conduct, adequacy of resources and services, healthcare delivery, accessibility of care, and cost of care). Secondary independent variables: Respondent, visit-related, and facility characteristics. Primary outcome measure: Patient satisfaction measured on a 1-10 scale. Methods: Indices of perceived quality of care were modelled using principal component analysis. Statistical associations between perceived quality of care and patient satisfaction were examined using random-effect ordered logistic regression models, adjusting for demographic, socioeconomic, visit and facility characteristics. Results: Average satisfaction was 6.9 on a 10-point scale for non-HIV services and 7.3 for HIV services. Favourable perceptions of health personnel conduct were associated with higher odds of overall satisfaction for non-HIV (OR=3.53, 95% CI 2.34 to 5.33) and HIV (OR=11.00, 95% CI 3.97 to 30.51) visits. Better perceptions of resources and services were also associated with higher odds of satisfaction for both non-HIV (OR=1.66, 95% CI 1.08 to 2.55) and HIV (OR=4.68, 95% CI 1.81 to 12.10) visits. Two additional dimensions of perceived quality of care-healthcare delivery and accessibility of care-were positively associated with higher satisfaction for non-HIV patients. The odds of overall satisfaction were lower in rural facilities for non-HIV patients (OR 0.69; 95% CI 0.48 to 0.99) and HIV patients (OR=0.26, 95% CI 0.16 to 0.41). For non-HIV patients, the odds of satisfaction were greater in hospitals compared with health centres/ posts (OR 1.78; 95% CI 1.27 to 2.48) and lower at publicly-managed facilities (OR=0.41, 95% CI=0.27 to 0.64). Conclusions: Perceived quality of care is an important driver of patient satisfaction with health service delivery in Zambia. The exit interviews were conducted between December 2011 and May 2012 across 16 Zambian districts as part of the Access, Bottlenecks, Costs, and Equity (ABCE) project. The details of this project are documented elsewhere and available online.26 A two-step stratified random sampling process was used to select health facilities. First, Zambia’s districts (72 at the time, currently 103) were stratified on the basis of average household wealth, population density and skilled birth attendance (SBA) coverage. One district was randomly selected from each wealth–population–SBA category, in addition to the capital, Lusaka. In each selected district, we selected all hospitals, two urban health centres, three rural health centres, and a quota of associated health posts. The exit interviews were conducted at a subset of the facilities selected for the overall ABCE project. Our study reports on interviews conducted at 104 facilities. Compared with all facilities in Zambia, we oversampled hospitals and urban health centres and undersampled rural health centres and health posts to allow for platform-specific analyses (see online supplementary appendix table 1). Our sample is representative of the Zambian population and health delivery system, except that we oversampled hospitals to allow for separate analyses of hospital data. The sample of patients who sought care was also skewed towards females, which is expected due to several factors including women seeking maternal health services and a higher HIV prevalence among women (15.1%) than men (11.3%).11 At each facility participating in the exit survey, 30 patients were systematically sampled as they exited. Sampling intervals varied from every patient to every four patients, depending on the patient volume reported by the facility manager. The sample size of 30 patients at each facility was estimated using the Kish method with the following assumptions: patient satisfaction rate of 10%, precision of 5%, α of 1%, design effect of two, and non-response rate of 20%. The estimated sample from the Kish method was further adjusted to allow for robust subgroup analyses (eg, HIV vs non-HIV; hospital vs health clinic; rural vs urban). Interviews were conducted over at least two days at each facility. Patients were required to be 15 years or older and in an appropriate physical and mental state to be eligible to complete the survey. If a patient was too young or otherwise ineligible, an eligible attendant was asked to answer on their behalf when possible. Verbal consent was obtained from all respondents, and surveys were conducted in a location where the facility staff and other patients were not present. Trained research assistants recorded exit interview responses electronically using the DatStat data collection software. On a daily basis, data were uploaded to a database accessible from Seattle, where they were continually verified for quality during the collection process. The median interview time was nine minutes. At each health facility, research assistants interviewed facility administrators to collect information about facility resources, staffing, management and practices. Facility level and management were verified against a facility roster provided by the Ministry of Health (MOH). The exit instrument drew questions from established patient exit and household surveys, which in-country partners tested and modified to fit the Zambian context. Demographic questions were based on the Zambian DHS.27 Questions about visit circumstances and costs were adapted from the World Health Survey.28 We measured patients’ overall satisfaction with the facility with the following question from the Consumer Assessment of Healthcare Providers and Systems Adult Visit questionnaire: Using any number from 1 to 10, where 1 is the worst facility possible and 10 is the best facility possible, what number would you use to rate this facility?29 30 The survey also captured how patients perceived the quality of specific aspects of the facility and its providers, based on a validated questionnaire developed by Baltussen et al31 that has been used in other developing settings.32 33 Patients were asked to rate 25 aspects of the facility on a five-point Likert scale: very bad, bad, moderate, good or very good. The majority of questions were answered by over 95% of patients, but we excluded five questions to which over 10% of patients responded ‘not applicable’, ‘don’t know’, or ‘decline to respond’. These five questions concerned: adequacy of doctors for women, ease of making payment arrangements, time doctors allow for patients, availability of good doctors, and provider’s follow-up with patients. We then used principal component analysis (PCA) with orthogonal rotation to examine the structure of the remaining 20 perceived quality questions (see online supplementary appendix table 2). The analysis identified five components with eigenvalues ranging from 0.94 to 7.8, which explained 62% of the variance. The factors aligned with theoretical domains and can be interpreted as: (1) health personnel practices and conduct, (2) adequacy of resources and services, (3) healthcare delivery, (4) accessibility of care, and (5) cost of care. The specific questions under each domain are listed in online supplementary appendix table 2. The factor with an eigenvalue under 1 (accessibility of care) was retained because the variables it contained were theoretically grouped and not otherwise represented. Cronbach’s α coefficients for each grouping ranged from 0.70 to 0.90, which met the generally accepted threshold of 0.70 and was comparable to or better than studies conducting similar exercises.34 To condense the information for each domain, we created a new variable that was the per cent of questions within the domain which the respondent rated ‘good’ or ‘very good’. We opted to examine the responses in this categorical manner rather than as continuous averages because (1) Likert scales from very bad to very good are not truly continuous and (2) research shows that patients typically rate facilities favourably, and therefore the important distinction is achieving the very highest ratings.35–37 If a patient did not answer a given question, we took the per cent among the questions that were answered. We used random-effects ordered logistic regression models to examine how overall satisfaction (rated from 1 to 10) was related to objective patient, facility and visit factors, as well as patient perceptions of specific aspects of care (measured with the 5-point Likert scale). The unit of analysis was the patient, and the outcome for all models was the patient’s overall rating of the facility out of 10 (described above in measuring satisfaction). An ordered model was selected because the outcome scale was ordered but not truly continuous. Additionally, since the outcome variable was skewed towards higher ratings, we grouped all responses below six into a single category for the purpose of the regression models (see online supplementary appendix figure 1). The first model examined how facility, patient and visit characteristics were associated with overall satisfaction. Independent variables were selected a priori based on relationships previously identified in the literature. Facility variables included facility type (hospital or health centre/post), location (urban or rural), and management (public or non-governmental organisation [NGO]/private). Demographic variables included age, self-rated overall health, ethnicity, sex, education level, and a binary indicator of whether the respondent was the patient or an attendant. Variables surrounding visit circumstances included travel time, wait time, and type of provider seen. We did not include whether or not the patient paid a user fee as this was largely determined by facility management—public and NGO facilities typically offer free services while private facilities often charge fees. The second model looked at how patients’ perceptions of particular domains of care related to their overall perception, to identify which aspects are most influential. The predictor variables in this case were the five summary perceived quality variables (described above in condensing perceived quality responses): health personnel practices and conduct, adequacy of resources and services, healthcare delivery, accessibility of care, and cost of care. Our final combined model included all of the facility, patient, visit, and perceived quality predictors from the first and second models. This allowed us to examine whether any facility, patient or visit characteristics were associated with overall satisfaction independent of how the patient rated specific aspects of care. All models included facility random effects to account for unmeasured facility characteristics, and we estimated robust standard errors (SEs) to account for intragroup correlation within facilities. Patients missing one or more covariates were excluded from all regression analyses. Our sample contained a substantial number of patients receiving HIV-related services; we analysed these patients separately from those receiving other services because HIV care may involve specialised staff, equipment and drugs, and because HIV often receives unique policy attention based on the large burden it poses in Zambia. We additionally conducted sensitivity analyses to test for effect modification by facility management, facility location, facility level and respondent identity (patient or attendant). To do this, we conducted the same analyses described above, stratified by the characteristic of interest, rather than by the HIV visit or not. Data management and analysis were conducted in Stata V.13.1.

Résumé de l’IA

Study Justification:

This study aimed to examine the associations between perceived quality of care and patient satisfaction among HIV and non-HIV patients in Zambia. The study was conducted to provide evidence on the importance of perceived quality of care in driving patient satisfaction with health service delivery in Zambia.

Highlights:

– The study included 2789 exiting patients from 104 primary, secondary, and tertiary health clinics across 16 Zambian districts.
– Perceived quality of care was measured using five dimensions: health personnel practice and conduct, adequacy of resources and services, healthcare delivery, accessibility of care, and cost of care.
– Patient satisfaction was measured on a 1-10 scale, with an average satisfaction of 6.9 for non-HIV services and 7.3 for HIV services.
– Favourable perceptions of health personnel conduct and adequacy of resources and services were associated with higher odds of overall satisfaction for both non-HIV and HIV visits.
– Two additional dimensions of perceived quality of care, healthcare delivery and accessibility of care, were positively associated with higher satisfaction for non-HIV patients.
– The odds of overall satisfaction were lower in rural facilities for both non-HIV and HIV patients.
– For non-HIV patients, the odds of satisfaction were greater in hospitals compared to health centers/posts and lower at publicly-managed facilities.

Recommendations:

Based on the study findings, the following recommendations can be made:

1. Improve health personnel practice and conduct to enhance patient satisfaction.
2. Enhance the adequacy of resources and services to improve patient satisfaction.
3. Focus on improving healthcare delivery and accessibility of care for non-HIV patients to increase satisfaction.
4. Address the disparities in satisfaction between rural and urban facilities.
5. Improve satisfaction at publicly-managed facilities by identifying and addressing the underlying factors.

Key Role Players:

To address the recommendations, the following key role players are needed:

1. Ministry of Health: Responsible for policy development and implementation.
2. Health facility administrators: Responsible for managing and improving facility resources, staffing, and practices.
3. Health personnel: Responsible for providing quality care and improving their practice and conduct.
4. Non-governmental organizations (NGOs): Collaborating with the government to improve healthcare delivery and access.
5. Researchers and academics: Conducting further studies and providing evidence-based recommendations.

Cost Items:

While the actual cost is not provided, the following cost items should be considered in planning the recommendations:

1. Training and capacity building for health personnel to improve their practice and conduct.
2. Investment in resources and services to ensure adequacy and quality.
3. Infrastructure development and improvement to enhance healthcare delivery and accessibility.
4. Outreach and awareness campaigns to address disparities in satisfaction between rural and urban facilities.
5. Monitoring and evaluation systems to track progress and ensure the effectiveness of interventions.

Force de la preuve

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a cross-sectional national exit survey conducted at 104 health clinics across 16 Zambian districts. The study used a large sample size of 2789 exiting patients and employed statistical analysis techniques such as principal component analysis and random-effect ordered logistic regression models. The study adjusted for demographic, socioeconomic, visit, and facility characteristics. The results show significant associations between perceived quality of care and patient satisfaction for both HIV and non-HIV patients. To improve the evidence, future studies could consider using a longitudinal design to assess changes in patient satisfaction over time and include a control group to compare satisfaction levels between different types of healthcare services.

Abstrait

The exit interviews were conducted between December 2011 and May 2012 across 16 Zambian districts as part of the Access, Bottlenecks, Costs, and Equity (ABCE) project. The details of this project are documented elsewhere and available online.26 A two-step stratified random sampling process was used to select health facilities. First, Zambia’s districts (72 at the time, currently 103) were stratified on the basis of average household wealth, population density and skilled birth attendance (SBA) coverage. One district was randomly selected from each wealth–population–SBA category, in addition to the capital, Lusaka. In each selected district, we selected all hospitals, two urban health centres, three rural health centres, and a quota of associated health posts. The exit interviews were conducted at a subset of the facilities selected for the overall ABCE project. Our study reports on interviews conducted at 104 facilities. Compared with all facilities in Zambia, we oversampled hospitals and urban health centres and undersampled rural health centres and health posts to allow for platform-specific analyses (see online supplementary appendix table 1). Our sample is representative of the Zambian population and health delivery system, except that we oversampled hospitals to allow for separate analyses of hospital data. The sample of patients who sought care was also skewed towards females, which is expected due to several factors including women seeking maternal health services and a higher HIV prevalence among women (15.1%) than men (11.3%).11 At each facility participating in the exit survey, 30 patients were systematically sampled as they exited. Sampling intervals varied from every patient to every four patients, depending on the patient volume reported by the facility manager. The sample size of 30 patients at each facility was estimated using the Kish method with the following assumptions: patient satisfaction rate of 10%, precision of 5%, α of 1%, design effect of two, and non-response rate of 20%. The estimated sample from the Kish method was further adjusted to allow for robust subgroup analyses (eg, HIV vs non-HIV; hospital vs health clinic; rural vs urban). Interviews were conducted over at least two days at each facility. Patients were required to be 15 years or older and in an appropriate physical and mental state to be eligible to complete the survey. If a patient was too young or otherwise ineligible, an eligible attendant was asked to answer on their behalf when possible. Verbal consent was obtained from all respondents, and surveys were conducted in a location where the facility staff and other patients were not present. Trained research assistants recorded exit interview responses electronically using the DatStat data collection software. On a daily basis, data were uploaded to a database accessible from Seattle, where they were continually verified for quality during the collection process. The median interview time was nine minutes. At each health facility, research assistants interviewed facility administrators to collect information about facility resources, staffing, management and practices. Facility level and management were verified against a facility roster provided by the Ministry of Health (MOH). The exit instrument drew questions from established patient exit and household surveys, which in-country partners tested and modified to fit the Zambian context. Demographic questions were based on the Zambian DHS.27 Questions about visit circumstances and costs were adapted from the World Health Survey.28 We measured patients’ overall satisfaction with the facility with the following question from the Consumer Assessment of Healthcare Providers and Systems Adult Visit questionnaire: Using any number from 1 to 10, where 1 is the worst facility possible and 10 is the best facility possible, what number would you use to rate this facility?29 30 The survey also captured how patients perceived the quality of specific aspects of the facility and its providers, based on a validated questionnaire developed by Baltussen et al31 that has been used in other developing settings.32 33 Patients were asked to rate 25 aspects of the facility on a five-point Likert scale: very bad, bad, moderate, good or very good. The majority of questions were answered by over 95% of patients, but we excluded five questions to which over 10% of patients responded ‘not applicable’, ‘don’t know’, or ‘decline to respond’. These five questions concerned: adequacy of doctors for women, ease of making payment arrangements, time doctors allow for patients, availability of good doctors, and provider’s follow-up with patients. We then used principal component analysis (PCA) with orthogonal rotation to examine the structure of the remaining 20 perceived quality questions (see online supplementary appendix table 2). The analysis identified five components with eigenvalues ranging from 0.94 to 7.8, which explained 62% of the variance. The factors aligned with theoretical domains and can be interpreted as: (1) health personnel practices and conduct, (2) adequacy of resources and services, (3) healthcare delivery, (4) accessibility of care, and (5) cost of care. The specific questions under each domain are listed in online supplementary appendix table 2. The factor with an eigenvalue under 1 (accessibility of care) was retained because the variables it contained were theoretically grouped and not otherwise represented. Cronbach’s α coefficients for each grouping ranged from 0.70 to 0.90, which met the generally accepted threshold of 0.70 and was comparable to or better than studies conducting similar exercises.34 To condense the information for each domain, we created a new variable that was the per cent of questions within the domain which the respondent rated ‘good’ or ‘very good’. We opted to examine the responses in this categorical manner rather than as continuous averages because (1) Likert scales from very bad to very good are not truly continuous and (2) research shows that patients typically rate facilities favourably, and therefore the important distinction is achieving the very highest ratings.35–37 If a patient did not answer a given question, we took the per cent among the questions that were answered. We used random-effects ordered logistic regression models to examine how overall satisfaction (rated from 1 to 10) was related to objective patient, facility and visit factors, as well as patient perceptions of specific aspects of care (measured with the 5-point Likert scale). The unit of analysis was the patient, and the outcome for all models was the patient’s overall rating of the facility out of 10 (described above in measuring satisfaction). An ordered model was selected because the outcome scale was ordered but not truly continuous. Additionally, since the outcome variable was skewed towards higher ratings, we grouped all responses below six into a single category for the purpose of the regression models (see online supplementary appendix figure 1). The first model examined how facility, patient and visit characteristics were associated with overall satisfaction. Independent variables were selected a priori based on relationships previously identified in the literature. Facility variables included facility type (hospital or health centre/post), location (urban or rural), and management (public or non-governmental organisation [NGO]/private). Demographic variables included age, self-rated overall health, ethnicity, sex, education level, and a binary indicator of whether the respondent was the patient or an attendant. Variables surrounding visit circumstances included travel time, wait time, and type of provider seen. We did not include whether or not the patient paid a user fee as this was largely determined by facility management—public and NGO facilities typically offer free services while private facilities often charge fees. The second model looked at how patients’ perceptions of particular domains of care related to their overall perception, to identify which aspects are most influential. The predictor variables in this case were the five summary perceived quality variables (described above in condensing perceived quality responses): health personnel practices and conduct, adequacy of resources and services, healthcare delivery, accessibility of care, and cost of care. Our final combined model included all of the facility, patient, visit, and perceived quality predictors from the first and second models. This allowed us to examine whether any facility, patient or visit characteristics were associated with overall satisfaction independent of how the patient rated specific aspects of care. All models included facility random effects to account for unmeasured facility characteristics, and we estimated robust standard errors (SEs) to account for intragroup correlation within facilities. Patients missing one or more covariates were excluded from all regression analyses. Our sample contained a substantial number of patients receiving HIV-related services; we analysed these patients separately from those receiving other services because HIV care may involve specialised staff, equipment and drugs, and because HIV often receives unique policy attention based on the large burden it poses in Zambia. We additionally conducted sensitivity analyses to test for effect modification by facility management, facility location, facility level and respondent identity (patient or attendant). To do this, we conducted the same analyses described above, stratified by the characteristic of interest, rather than by the HIV visit or not. Data management and analysis were conducted in Stata V.13.1.

Matériaux

N/A

Innovations

The study mentioned in the description focuses on examining the associations between perceived quality of care and patient satisfaction among HIV and non-HIV patients in Zambia. The study collected data through exit surveys conducted at various health clinics across 16 Zambian districts. The study analyzed the data using statistical methods such as principal component analysis and random-effect ordered logistic regression models.

Based on the findings of the study, some potential recommendations for improving access to maternal health could include:

1. Enhancing health personnel practices and conduct: Improving the behavior and conduct of healthcare providers can positively impact patient satisfaction. Training programs and continuous professional development initiatives can be implemented to enhance the skills and attitudes of healthcare providers.

2. Ensuring adequacy of resources and services: Adequate availability of resources and services is crucial for providing quality maternal health care. Investments in infrastructure, medical equipment, and supplies can help improve the overall quality of care.

3. Improving healthcare delivery: Efficient and effective healthcare delivery is essential for ensuring timely access to maternal health services. Streamlining processes, reducing waiting times, and implementing evidence-based practices can enhance the overall experience for patients.

4. Enhancing accessibility of care: Improving access to maternal health services, especially in rural areas, is important for reducing disparities in healthcare. Initiatives such as mobile clinics, telemedicine, and community health worker programs can help improve access to care in remote areas.

5. Addressing cost of care: High healthcare costs can be a barrier to accessing maternal health services. Implementing policies to reduce out-of-pocket expenses, providing financial assistance programs, and strengthening health insurance coverage can help alleviate the financial burden on patients.

It is important to note that these recommendations are based on the findings of the specific study mentioned and may need to be tailored to the specific context and needs of each healthcare system.

AI Innovations Description

The recommendation that can be developed into an innovation to improve access to maternal health based on the provided description is to focus on improving the perceived quality of care and patient satisfaction among HIV and non-HIV patients in Zambia. This can be achieved by implementing the following strategies:

1. Enhance health personnel practices and conduct: Provide training and support to healthcare providers to improve their communication skills, empathy, and professionalism when interacting with patients. This can help create a positive patient-provider relationship and increase patient satisfaction.

2. Ensure adequacy of resources and services: Invest in improving the availability and quality of healthcare resources, such as medical equipment, medications, and facilities. This can help address any gaps in the provision of maternal health services and improve patient satisfaction.

3. Improve healthcare delivery: Streamline and optimize the delivery of maternal health services to ensure efficient and timely care. This can involve implementing standardized protocols and guidelines, improving coordination among healthcare providers, and reducing waiting times for appointments and procedures.

4. Enhance accessibility of care: Address barriers to accessing maternal health services, particularly in rural areas. This can involve expanding healthcare facilities and services in underserved areas, improving transportation options, and implementing telemedicine or mobile health initiatives to provide remote access to healthcare.

5. Address cost of care: Explore strategies to make maternal health services more affordable and accessible to all patients, regardless of their socioeconomic status. This can include implementing health insurance schemes, subsidizing healthcare costs for vulnerable populations, and promoting financial assistance programs.

By focusing on these recommendations and implementing innovative solutions, such as leveraging technology and community engagement, access to maternal health can be improved, leading to better health outcomes for women and their babies in Zambia.

AI Innovations Methodology

The study described in the provided text focuses on examining the associations between perceived quality of care and patient satisfaction among HIV and non-HIV patients in Zambia. The objective is to understand how different dimensions of perceived quality of care, such as health personnel practice and conduct, adequacy of resources and services, healthcare delivery, accessibility of care, and cost of care, impact patient satisfaction.

To simulate the impact of recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Identify potential recommendations: Conduct a comprehensive review of existing literature, policies, and best practices to identify potential recommendations that have been proven effective in improving access to maternal health. These recommendations could include interventions such as increasing the number of skilled birth attendants, improving the availability and quality of maternal health services, implementing community-based interventions, and strengthening referral systems.

2. Define indicators: Determine the key indicators that will be used to measure the impact of the recommendations on improving access to maternal health. These indicators could include metrics such as the number of women accessing antenatal care, the percentage of women receiving skilled birth attendance, the reduction in maternal mortality rates, and the increase in patient satisfaction scores.

3. Develop a simulation model: Create a simulation model that incorporates the identified recommendations and their potential impact on the defined indicators. This model should take into account factors such as population demographics, healthcare infrastructure, resource availability, and geographical distribution.

4. Data collection: Gather relevant data to populate the simulation model. This may include data on the current state of maternal health access, demographic information, healthcare facility capacities, and resource availability. Data can be collected through surveys, interviews, and existing databases.

5. Model calibration: Calibrate the simulation model using the collected data to ensure that it accurately represents the current state of maternal health access. This may involve adjusting parameters and assumptions within the model to align with the observed data.

6. Scenario analysis: Conduct scenario analyses using the calibrated simulation model to simulate the impact of different combinations of recommendations on improving access to maternal health. This can help identify the most effective and feasible interventions for improving access.

7. Evaluation and validation: Evaluate the results of the simulation model against real-world data and validate the model’s accuracy. This may involve comparing the simulated outcomes with actual outcomes observed in similar contexts or conducting sensitivity analyses to test the robustness of the model.

8. Policy recommendations: Based on the simulation results, generate policy recommendations that prioritize the most effective and feasible interventions for improving access to maternal health. These recommendations should consider factors such as cost-effectiveness, scalability, and sustainability.

By following this methodology, policymakers and healthcare stakeholders can gain insights into the potential impact of different recommendations on improving access to maternal health. This can inform evidence-based decision-making and resource allocation to address the challenges in maternal healthcare access.

Auteur

Dima Health

Statistics:

Citations: 26

Identifiers:

DOI: 10.1136/bmjopen-2015-009700

Research Areas:

Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Quality of Care, Social Determinants, Workforce

Study Countries:

Zambia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative

Participants Gender:

Male & Female

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