American Journal of Sociological Research
p-ISSN: 2166-5443 e-ISSN: 2166-5451
2016; 6(2): 49-55
doi:10.5923/j.sociology.20160602.02

Otiato S. O.1, Omondi D. O.2, Abong’o B. O.3
1Department of Public and Health, Maseno University, Maseno Township, Kenya
2Kenya Nutritionists and Dieticians Institute and Department of Nutrition and Health, Maseno University, Department of Nutrition and Health, Maseno University, Maseno Township, Kenya
3Department of Biomedical Sciences, Maseno University, Maseno Township, Kenya
Correspondence to: Omondi D. O., Kenya Nutritionists and Dieticians Institute and Department of Nutrition and Health, Maseno University, Department of Nutrition and Health, Maseno University, Maseno Township, Kenya.
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Background: Low school completion rates for girls could be attributed to gender stereotypical biases and preferential treatments usually accorded to male as opposed to female gender in the education system. School health policies have been rolled out in primary schools in Kenya to ensure equity among boys and girls. However, girls still face challenges related to gender issues than boys. This study explored the extent of implementation of gender perspectives in school health policy for primary schools in Usigu division. Materials and Methods:The study was conducted in rural primary schools along the beaches in Usigu Division using cross-sectional analytical design. A sample of 338 girls selected through stratified random sampling, from STD 4-8 was used. Data was collected using structured questionnaires, analysis done using descriptive and inferential statistics mainly hierarchical regression with Principal Axis Factor Analysis Techniques. Results: Implementation threshold of gender policy as perceived by the STD 4-8 respondents (girls) was 20.79% of total variance explained by the three different gender factor loadings based on principal factor axis factoring. Gender factor loading 1 made up of one item,“Our parents are usually informed about gender issues affecting girls through drama, music and other channels” which explained a variance of 7.05% of the gender implementation was the most implemented, followed byGender factor loading 2 with a variance of 6.89%and made up of two implementation indicators: “Our school has enough toilets as per set standards which easily cater for girl's special needs during emergency menstrual cycles” and “Girls in our school are provided with adequate sanitary pads for use during menstrual cycles”. Gender factor loading 3followed very closely with a variance of 6.85% and constituted two items: “In our school, discussion of certain topics by fellow pupils and teachers carefully avoid making girls feel less important” and, “Parents and teachers in our school usually avoid comments and behavior that make girls feel useless or of no importance”. Conclusions: The findings revealed that level of implementation of the gender component of the current policy is still weak based on 50% factor loading out of 10 elements drawn from the policy. The study however, recommends that more evaluations focusing on the broader themes of the policy should be conducted in the wider contexts and further explorations be done to explain non extraction of other factors.
Keywords: Gender perspectives, School health policy, Primary school girls
Cite this paper: Otiato S. O., Omondi D. O., Abong’o B. O., Gender Perspectives in School Health Policy Implementation among Girls in Usigu Division Primary Schools, Siaya County, Kenya, American Journal of Sociological Research, Vol. 6 No. 2, 2016, pp. 49-55. doi: 10.5923/j.sociology.20160602.02.
Where: n = minimum sample size (for population >10,000) required.Z = the standard normal deviate at the required confidence level, (set at 1.96 corresponding to 95%, Confidence level adopted for this study).p = population proportion estimated to be girls in Usigu which now stands at approximately 50% q = 1-pd = the degree of accuracy required (was set at 0.05)
However, since the targeted population was 1531eligible pupils which are <10,000, the final sample size (nf) was adjusted as follows:
Where; n f= desired sample size (when target population is less than 10,000) n = desired sample size (when target population is greater than 10,000) N = the desired sample size (target population)nf = 384 ÷ {1+ (384/1531)} =338 (plus 10% expected non-response) Stratified random sampling technique was used to select pupils to be included in the study. Schools were stratified into either public or private before randomly selecting girls proportionately according to sample size. Sampled schools were visited one week before the actual data collection and children were issued with consent forms which their parents or guardians were to sign showing their approval for their children to participate in the intended research. The forms were to be returned on the actual day of data collection and any child whose parents did not consent or who personally did not assent to participate in the study was excluded.The children were responded to questions after reading out the questions loudly alongside the expected responses for each child in confidence and a language that was perceived to be understood by all the respondents and coding their responses appropriately.Measurement of variables focused on 10 key indicators drawn from the school health policy tested using a 5-point likert scale ranging from 1-strongly agree and 5=strongly disagree. Dependent (outcome) variables were all possible gender categories as perceived by girls based on factor loadings within gender framework in school health policy measured by the ten indictors generated from policy guidelines (2009). Independent variables focused on single indicators of gender perspectives of school health policy. Analysis adopted use of descriptive and inferential statistics mainly hierarchical regression with Principal Axis Factor Analysis techniques to show the relationship between the factor loads (which depicted opinion agreement on gender areas being implemented) and key isolated gender policy guideline measures. Factor extraction zeroed in on Varimax rotation and Eigen values equivalent to 1.
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![]() | Figure 1. Graph showing factor plots in rotated factor space |
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