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The Gender Belief System of Maternal Health Service Stakeholders in

Partido, Philippines

Bernadette G. Gumba

Partido State University

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000245

Received: 20 October 2025; Accepted: 26 October 2025; Published: 17 November 2025

ABSTRACT

The study analyzed the gender belief system of Maternal Health Service (MHS) stakeholders in the Partido

District, Philippines. It surveyed the socio-demographics of respondents; tested their gender bias; and analyzed

gender bias score in relation to selected socio-demographic variables. The bias score was analyzed using

Pearson’s correlation and regression analysis. The respondents were not extremely biased but did not have strong

sense of gender equality. Beneficiaries had higher bias score than decision-makers/ implementers. There were

outliers among decision-makers/ implementers. Correlation statistics disclosed that a 6.66-point decrease in bias

score was expected for every level increase in education. The regression results implied that 19% of the variation

in bias score was attributed to education. This may be explained by the exposure of educated individuals to

varied, new concepts and theories. Education opens the mind to a whole new set of cultures and norms which

may challenge the individual to embrace new belief systems.

Keywords: Gender belief system, gender bias and socio-demographics, maternal health services

INTRODUCTION

Gender disparities are a persistent form of inequality in every country. Despite remarkable progress in some

areas, no country in the world—rich or poor—has achieved gender equality. All too often, women and girls are

discriminated against in health, in education, at home and in the labour market—with negative repercussions for

their freedoms (UNDP, 2020).

According to the 2023 GSNI (Gender Social Norms Index) of the UNDP (2020), about half of the world’s men

and women feel that men make better political leaders, and over 40 percent feel that men make better business

executives and that men have more right to a job when jobs are scarce. Moreover, 28 percent think it is justified

for a man to beat his wife. The GSNI measures how social beliefs obstruct gender equality in areas like politics,

work, and education, and contains data from 75 countries, covering over 80 percent of the world’s population

(UNDP, 2020).

A study of the BMGF (Bill and Melinda Gates Foundation, 2020) found substantial support for a relationship

between gender-based risk factors and MNH (Maternal and Newborn Health) outcomes and in the use of MNH

services. Based on this, the current research tackled the gender belief sytem of beneficiaries, implementers and

decision-makers in MHS (Maternal Health Services). It considered the gender beliefs of stakeholders as a

gender-based risk factor. It assumed that the mindset of stakeholders is linked with their attitude and behavior

toward MHS. Moreover, women and girls are still discriminated against in health service programs (UNDP,

2020). A study of Ghebreyesus (2019) confirmed that women health workers are concentrated into lower status,

lower paid and often unpaid roles. Boniol et al. (2019) added that 24 million of the 28.5 million nurses and

midwives globally are women. More men reach leadership positions, leaving women underrepresented in senior,

higher-paid roles.

The foregoing discussion explains why this study utilized a gender lens as gender is an aspect that is often

ignored but deeply embedded in project implementation and evaluation. The study examined the relationship

between the gender-based belief system of couples, decision-makers and implementers of MHS vis-à-vis

selected sociodemographic variables.

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

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Theoretical Framework

This study is anchored on the theories of Rettig (2017), Kite & Deaux (1987), Mencarini (2014), Valved (2021),

Hogenboom (2021), Joel (2015) and Rippon (2019) whose assertions are discussed below.

A belief system is a mindset and way of thinking that manifest in a person’s behavior, attitude, and way of dealing

with people and structures, and formulating opinions and decisions on issues surrounding the individual (Rettig,

2017). In a broad sense, the gender belief system is defined as a set of beliefs and opinions about males and

females and about the purported qualities of masculinity and femininity (Kite & Deaux, 1987). This belief system

includes stereotypical views about men and women and attitudes toward appropriate gender roles, based on an

assumption that what is not feminine is necessarily masculine, and vice versa, and that a person who is either

masculine or feminine in one aspect of behavior is similarly masculine or feminine in other aspects of behavior

(Mencarini, 2014).

Within cultures, people develop a shared gender belief system that includes the content of gender stereotypes,

masculinity and femininity, and attitudes toward people who deviate from traditional gender roles (Valved et al,

2021). As neuroscientist and author Gina Rippon (2019) explains, the fact that we live in a gendered world itself

creates a gendered brain. It creates a culture of boys who feel conditioned to behave in more typically masculine

traits and girls to behave in feminine ways, or they may get excluded by peers. If we focus on differences, it also

means, as Rippon says, we begin to accept myths such as boys being better at science and girls at caring

(Hogenboom, 2021).

In a study analyzing 1,400 brain scans, neuroscientist Daphna Joel et al (2015) found extensive overlap between

the distribution of females and males for all grey matter, white matter and connections assessed. That is, overall,

females and males are more similar to each other than different. One study even showed that women acted just

as aggressively as men in a video game when they were told their gender would not be disclosed, but less so

when told the experimenter knew if the participants were male or female. This means that in order for there to

be any significant change, people have to first understand their biases and be mindful of when their

preconceptions do not fit into the behaviors they see. Even small differences of what they expect of girls versus

boys can build up over time (Hogenboom, 2021).

Fig.1. Gender belief system of MHS stakeholders vis-à-vis their socio-demographics

Note: Designed by the author based on the research objectives

As illustrated in Figure 1, this study examined the gender belief system of three major stakeholders in MHS. The

decision-maker provides the structure and guideline of the services which become the basis of implementation,

the implementer dispenses the services, and the beneficiary receives the services. In the dynamics of their

relationship, the gender belief system of individuals influences their behavior, attitude, social interaction and

decisions – the policies that the government official makes, the way the services are executed by the implementer,

and the response of the beneficiary, as well as how the implementer and beneficiary deal with each other (BMGF,

2020). The study looked into the relationship of gender, age and education with the gender belief system.

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Objectives

This study analyzed the gender belief system of reproductive-age mothers and fathers, implementers and

decision-makers of MHS (Maternal Health Services) in the Partido District, Camarines Sur, Philippines. To

accomplish this, the following specific objectives were set:

1. Survey a set of sociodemographic variables of MHS stakeholders;

2. Test the gender belief system of selected reproductive-age mothers, reproductive-age fathers, and

reproductive couples through the gender bias score;

3. Test the gender belief system of decision-makers and implementers of MCHS in selected local communities

through the gender bias score;

4. Analyze the relationship of selected socio-demographic variables with the gender belief system;

METHODOLOGY

This section presents the details of how the study was conducted, how the measurable indicators were calculated,

and how the data was analyzed.

Study Design

The study utilized quantitative method to determine the gender belief system through a gender bias test. One-

on-one interview was conducted to facilitate the test using a structured questionnaire culled from the Harvard

University’s Implicit Gender-Career Bias Test (Harvard, 2011) and the 2015 Commonwealth of Learning

Gender-Bias Quiz (COL, 2022). The test was composed of yes-or-no questions, with 61 items for a female

respondent and 63 for male. The tools were consolidated and revised to fit the context of rural and relatively

low-income living, and translated to Bicol language. Correlation statistics was used to analyze the relationship

between selected sociodemographic variables and gender bias scores. Qualitative method was employed to

examine the sociodemographics and results of the gender bias test.

Study Population

The respondents were selected from three of the poorest municipalities in the Province of Camarines Sur,

Philippines – Siruma, Garchitorena and Presentacion. The respondents were clustered as beneficiaries, decision-

makers and implementers. For the beneficiaries, there were 100 sample respondents. For the decision-makers,

there were 30 who were officials directly involved in health committees. For the implementers, 30 rural health

personnel or barangay health workers were selected. Respondents were selected at random using multi-stage

sampling method and the random tool of the Microsoft Excel Program.

Sample Size

The table below shows the total number of households per municipality covered, the number of sample barangays

for each, the number of sample beneficiaries, decision-makers and implementers. The household data were taken

from the website of the Philippine Statistics Authority (PSA, 2016). The distribution of samples was based on

the percentage share of each municipality on the total number of households i.e. 40% for Garchitorena, 31% for

Presentacion and 29% for Siruma.

The sample size of the beneficiaries was determined based on the Criteria Sampling Technique (Shaheen et al.,

2018). This sampling method was used to collect data from purposeful samples which, in the context of this

study, were the reproductive-age mothers and fathers as defined earlier. This specific criterion excluded a

relatively substantial portion of the population such as those outside the reproductive age, or those whose

children are already beyond five years old, or those who have no children. According to Shaheen et al. (2018),

this sampling method is strategic because it yields the best information and impact on the findings. Logical

generalizations were made from the evidence provided by the sample.

The sample size of the decision-makers and implementers was much fewer, which was determined based on the

Typical Case Sampling (Shaheen et al., 2018). The typical cases were the knowledgeable participants. In the

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)

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context of this study, the matters faced by the decision-makers on health services and the job description of

health personnel were generally similar, and therefore these individuals knew what was typical insofar as their

functions were concerned.

Study Plan

The study combined written document analysis with examination of data from primary sources. One-on-one

interviews were conducted with three clusters of respondents using a structured questionnaire which includes a

gender bias test culled from existing literature. The gender bias scores were correlated with selected socio-

demographic variables of respondents and with the indicators of MCHS.

The gender-based beliefs were surveyed in three clusters: the beneficiaries of the maternal and child health

services, the decision-makers and the implementers of the program. The beneficiaries were selected from

reproductive-age mothers, reproductive-age fathers and reproductive couples. The decision-makers in turn were

selected from among the barangay officials while the program implementers from the personnel of the Rural

Health Unit or Barangay Health Station.

The results of the gender bias test were assessed based on the theories of Rippon (2019), Hogenboom (2021)

and Joel (2015). In the study, “Yes” answers were a manifestation of a belief system that is biased. Rippon (2019)

theorized about a gendered brain which focuses on the differences or myths, such as boys are better at science

while girls at caring. Hogenboom (2021) theorized about gendered ideas and self-assumptions which creates a

belief system that draws a line between what a male or female is not, cannot do, or should not do, and thereby

reflects a mindset that espouses differences among individuals mainly because of gender. On the other hand,

“No” answers demonstrated a conviction toward gender equality which is open to all possibilities and

opportunities in life regardless of gender. “No” answers were therefore a manifestation of a belief system that is

relatively gender equal. As Joel (2015) theorized, overall, females and males are more similar to each other than

different.

The gender bias score was calculated as follows – number of items with “Yes” answer / total number of items.

Based on the foregoing discussion, high scores reflect high degree of gender bias. The scores were classified

according the following levels:

76-100 High bias

51-75 Higher-middle bias

26-50 Lower-middle bias

0-25 Low bias

RESULTS AND DISCUSSION

This section tackles the socio-demographic profile of selected stakeholder-respondents, the gender bias score,

the results of statistical analysis, and the interpretation of results.

Profile of Respondents

Table 1. Profile of the decision-maker, implementer and beneficiary

Indicator

Decision-maker

Implementer

Beneficiary

Gender

Female

Male

Total

100

Age

16-25

26-35

36-45

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46-55

56-above

Total

100

Education

Elementary

High school

College

Graduate degree

Total

100

Source: Data gathered from the study

The cluster of decision-makers was composed of the local chief executive, members of the local health health

board, coordinator of the municiplan planning and development, and representative of the health department.

The investigation intended that among the respondents, one-half were female and one-half were male. However,

during the data-gathering, the researcher found that this group was dominated by males. This confirms the study

of Ghebreyesus (2019) and Boniol et al. (2019). So the females had to be targeted. Despite the targeting, the total

respondents were still 53% male. Moreover, majority of the decision-makers were 46 years and above, and had

college education.

The group of implementers was composed of the barangay health workers, human resource health personnel,

municipal nutrition action officer, and local nurses. The intention of getting 50% female and 50% male among

the respondents was not fulfilled because this category was dominated by females. This confirms the study of

Ghebreyesus (2019) and Boniol et al. (2019). Despite tracing the male implementers, the total respondents were

90% female. Additionally, majority were 26-45 years old, and had college education.

The beneficiaries were those of reproductive age and married or with partner. They have children of 0-5 years

old and/or expecting to be a parent within nine months when the study was conducted. As intended, there were

50 females and 50 males and majority were 26-40 years old. Furthermore, majority had elementary and high

school education.

Gender Belief System

Table 2. Average gender bias score of decision-maker/ implementer and beneficiary, municipal level

Garchitorena

Presentacion

Siruma

Average

Decision-maker/ Implementer

Beneficiary

Average

Overall, the beneficiaries had higher average gender bias score than the decision-makers/ implementers. The

belief system of the former was more inclined toward a presumed difference among individuals because of

gender. This finding was consistent across the three municipalities, with those in Presentacion exhibiting the

highest average bias score of 72, followed by Garchitorena with 65 and Siruma with 62. Among the decision-

makers/ implementers, those in Siruma showed the lowest average bias score of 31, followed by Presentacion

with 45 and Garchitorena with 52. Collectively, the beneficiaries had a high average bias score of 66 as compared

with only 43 among the decision-makers/ implementers.

None among the decision-makers and implementers of Garchitorena and Siruma had above 75 bias score, while

there was one in Presentacion with a bias score of 100. Siruma had the greatest number of 16 beneficiaries with

high bias score. One of whom had a score of 97, one had 94 and two had 86. This was followed by Presentacion

with 15 beneficiaries having high gender bias. Three of whom had 87 and two had 86. In Garchitorena, there

were nine beneficiaries with high gender bias. Among these, one had 97, one had 92, one had 90, one had 87,

and one had 86.

There was a total of 10 decision-makers and implementers with low gender bias. Most of whom were in Siruma,

seven of them – one with a bias score of 11 and one with 15. Two of the 10 were in Presentacion – one with a

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bias score of 16 and one with 23. The other one of the 10 was in Garchitorena with a bias score of 8. None among

the beneficiaries of Garchitorena and Presentaction had a low gender bias score, while there were two in Siruma,

one with a bias score of 15 and one with 20.

Table 3. Distribution of respondents according to gender bias score

Score

Level of bias

Sub-totals

Totals

Decision- maker/ Implementer

Beneficiary

76-100

High

51-75

Higher-middle

26-50

Lower-middle

0-25

Low

Totals

100

160

As illustrated in the table, 25 percent of decision-makers/ implementers had lower-middle bias score and a total

of 49 percent with lower-middle and higher-middle. Of the beneficiaries, 56 percent had gender bias score of

higher-middle and a total of 86 percent with higher-middle and high.

Relationship Between Socio-Demographic Variable and Gender Bias Score

The results of the linear regression or Ordinary Least Square regression are given in Table 10. OLS regression

was used because the dependent variable, gender bias score, is continuous. As shown in the table, only education

had highly significant coefficient at 5% level of significance and is considered as significant predictor of bias

score. Age and gender are not significant predictors.

As observed, the education variable had a negative coefficient of -6.66, which suggests that an increase in the

level of educational attainment will result in lower bias score. Furthermore, a 6.66 decrease in bias score is

expected for every level increased in education. Hence, this study revealed that respondents with higher

educational attainment are more likely to have lower bias score.

The regression model is significant (p=0.0002) with R2 of 0.19, which implies that 19% of the variation in

gender bias score is attributed to the independent variables, particularly, educational attainment contributing

significantly to bias score. All assumption of the OLS regression were satisfied with normality of data

(Kolmogorov test, p=0.5113), heteroscedasticity, p>0.05 and non-violation of multicollinearity (VIFs< 10).

CONCLUSIONS

Results of the gender bias test revealed many remarkable findings. First, majority of the respondents were able

to decide swiftly which item in the test they agreed or disagreed with. The respondents took an average of 15-

20 minutes to complete the tool. Second, it may be concluded that collectively, beneficiaries, implementers and

decision-makers were not extremely gender biased but also did not have strong sense of gender equality.

Respondents held a conviction that women and men are different in behavior, attitude and thinking, which

extends to a belief that women and men do not have equal opportunies to lead the life they want.

A third observation was that the beneficiaries had higher bias score than the decision-makers and implementers.

Fourth, there were notable outliers among the decision-makers and implementers. Outliers were respondents

with abnormally high or low bias score. The study posted the following remarkably low bias – a Municipal

Mayor with only 8 points, a Rural Sanitation Inspector with 11 points, a Municipal Health Officer who holds a

medical degree with 15 points and a Registered Nurse with 16 points. This was a good sign because the mindsent

of decision-makers and implementers significantly influence the disposition of MNH as claimed in the study of

BMGF (2020). However, the study also recorded alarmingly high bias score among decision-makers and

implementers – a Barangay Health Worker registered a score of 100 points, a Registered Nurse with 75 and a

Barangay Captain with 75.

Correlation statistics disclosed that a 6.66-point decrease in bias score was expected for every level increase in

education. Hence, this study concluded that respondents with higher educational attainment were more likely to

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have lower bias score. The results of regression analysis implied that 19% of the variation in the gender bias

score was attributed to educational attainment. This may be explained by the exposure of educated individuals

to varied, new concepts and theories particularly in the social sciences. Education opens the mind to a whole

new set of cultures and norms from within and outside the country which may challenge the individual to

embrace new belief systems that would maximize her/his potentials.

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