Assess the Effectiveness of Need Based Biomedical Waste Management Module on its Practice by the Primary Health Centres in Bangalore Rural District, Karnataka

 

Prof. Dr. Nagarajappa D.

Principal, BVVS, Sajjalashree Institute of Nursing Science, Navanagar, Bagalkot-587102, Karnataka, India.

 

ABSTRACT:

Methods and materials: Quasi-experimental research design was used to conduct the study, pre and post test without control group approach was used to find out the “effectiveness of need based waste management module on the practice of Primary Health Centres (PHCs) on Bio Medical Waste Management (BMWM)”. The study was conducted on 45 PHCs in Bangalore rural district by using purposive sampling technique and data were collected through rating scale.

Results: The total mean score was 16.3±4.4 which is 10.73% of maximum practice score (PS) revealing very poor practice on BMWM in PHCs of all four taluks before intervention. Out of 45 PHCs the overall mean score was 13.38±3.54 which is 8.8% for Doddaballapura taluk, whereas in post Intervention Observation (IO) the mean score was 139.3±3.91 which is 91.64% for Nelamangala taluk revealing excellent (82.95%) improvement in practice on BMWM. Overall comparison between pre and 3^rd post IO mean, SD and mean% of PS on BMWM at various taluk during pre IO shows that out of 45 PHCs the mean score (16.3±4.4) which is 10.73%, whereas in 3^rd post IO the mean score was (137.1±3.04) which is 90.2% depicting excellent effectiveness of waste management module in all sample taluks.

Area wise practice on BMWM during pre IO shows that out of five areas the overall mean score was highest (15.89±0.42) which is 10.47%, whereas in 3^rd post IO the mean score was (137±0.8) which is 90% shows excellent effectiveness of module in all areas.

Highly significance difference was found between pre and post intervention observations PS of PHCs on BMWM. No significant association was found between PS of PHCs in post intervention observations when compared to the selected extraneous variables of PHCs on BMWM whereas, significant association was found between the duration of service of MO in the PHCs when compared to post IO PSs.

Conclusion:  Biomedical waste management module was effective in all sample PHCs.

 

 

BACK GROUND THE STUDY:

Everything is made for a defined purpose, “anything which is not intended for further use is termed as waste” (Park, 2009).

The scientific and industrial era in concert with the demands of a burgeoning population has caused rapid turnover of new products resulting in a rapid proliferation of urban solid waste. India’s population has been steadily increasing since 1921 and it is currently increasing at the rate of 16 million each year. India’s population was 238 million in 1901, doubled in 60 years to 439 million (1961); doubled again in only 30 years to reach 846 million by 1991. India’s population crossed the one billion mark on the 11^th of May 2000 and is projected to reach 1.53 billion by the year 2050 (Ministry of Health and Family Welfare, 2006).

 

As the population increases the production of waste also increases. With the growth of hospitals and health care centres in response to rapid population growth, the problem of safe handling and disposal of biomedical wastes also increased. The 20^th century witnessed the rapid mushrooming of hospitals in public and private sectors in urban as well as in rural areas, dictated by the needs of the expanding population. The advent and acceptance of disposables has made the generation of biomedical waste a significant factor in present hospitals and health care centres. However, biomedical waste is the common source of many communicable diseases including HIV and Hepatitis-B and C, which is a major concern around the world (Park, 2009).

 

With the advent of the 21^st century and the increased utilization of disposable materials that have been implemented to reduce the rates of infectious diseases, it is of utmost importance to manage hazardous biomedical waste in order to advert significant untoward consequences in the form of morbidity and mortality. Health care institutions which are responsible for the safe care of the population are producing voluminous quantity of rubbish, garbage and biomedical waste each day from wards, operation theatres and outpatient areas. Proper management of biomedical waste is essential to maintain hygiene, aesthetics, cleanliness and control of environmental pollution (Baghotia, 2004).

 

NEED FOR THE STUDY:

To protect the environment and health of the community, the ministry of Environment and Forest of India has notified bio-medical waste (Management and Handling) Rules in the year 1998. According to the bio-medical waste (Management and Handling) Rules (1998), “Bio-medical waste means any waste, which is generated during the diagnosis, treatment or immunization of human beings or animal or in research activities pertaining thereto or in the production or testing of biological: and including all categories of waste”.

 

The cross-sectional study reveals that the waste produced in the course of healthcare activities carries a higher potential for infection and injury than any other type of waste. Inadequate and inappropriate knowledge of handling of biomedical waste may have serious health consequences and a significant impact on the environment as well. The importance of training regarding biomedical waste management needs emphasis; lack of proper and complete knowledge about biomedical waste management impacts practices of appropriate waste disposal (Mathur,  Dwivedi, Hassan, and Misra, 2011). There are no defined methods for handling and disposal of these wastes, starting from the personnel responsible for collection through those who transport the wastes to the disposal site (Bdour, Altrabsheh, Hadadin, and Al-Shareif, 2007).

Biomedical waste management has diverse ramifications as it affects the health of the patients and health care providers (doctors, nurses, waste handlers). It also affects the environment, public health and sanitation (Agarwal, 2002). A study by Rao and Garg (1994) mentioned that the hospital waste is becoming increasingly complex due to changing technologies and increase in the services that hospitals perform for the community.

 

The management of biomedical waste is still in its infancy stage all over the world. The reason may be due to the lack of awareness. Hence, resource material on bio-medical waste management for hospital staffs including nurses is the need of the hour. The city of Bangalore generates about 2500 tones of solid waste every day, which includes biomedical waste. The disposal of this type of waste in a scientific manner is the need of the hour (Pruthvish, 1999).

 

A descriptive survey done in Bangalore revealed that the quantity of solid wastes generated in hospitals and nursing homes generally varied from half to 4kg per bed per day in Government hospitals, half to 2 kg per bed, per day in private hospitals, and half to 1 kg per bed, per day in nursing homes. The total quantity of hospital waste generated in Bangalore is about 40 tonnes per day. Out of this, nearly 45 to 50 % is infectious (Rao, 1998). There is no system for medical waste management in Gaza. Segregation is done only for sharps and there are no colour-coded bags. Biomedical waste is stored and disposed of with domestic waste in primary health care clinics (Massrouje, 2001).

 

In India hospitals and other health care establishments are not well equipped to handle the enormous amount of biomedical waste (Singh, Biswas, and Sharma, 2007). Diaz, Savage, and Eggerth (2005) stated that the inappropriate treatment and final disposal of the wastes can lead to adverse impacts on public health, to occupational health and safety, and to the environment. The management of these wastes is usually delegated to poorly educated laborers who perform most activities without proper guidance and sufficient protection.

 

There is an urgent need to raise the awareness amongst all concerned. Information can be disseminated by organizing seminars, workshops, practical demonstrations, group discussions, lectures etc. It is vital to formulate an effective education and training programs specific for different target groups involved in biomedical waste handling and management (Singh, Biswas, and Sharma, 2007).

 

OBJECTIVES:

1.      To assess the;

a.       Existing waste management practices of Primary Health Centres (PHCs).

b.      Effectiveness of need based Video Assisted Teaching Module (VATM) on Bio Medical Waste Management (BMWM) of the PHCs.

2.      To compare the effectiveness of need based VATM with the extraneous variables of the PHCs.

 

METHODS AND MATERIALS:

Quasi-experimental (Pre-experimental) design with experimental approach was selected to evaluate the effectiveness of a need based structured module on Bio-Medical Waste Management (BMWM) practices in Primary Health Centres (PHCs). The design selected was a pre and post-test design without control group with a longitudinal prospective approach to collect the required data. The study was conducted at PHCs in Bangalore Rural District, Karnataka. All the 45 PHCs coming under the Bangalore Rural District and 392 health workers including MOs of the PHCs under study were included as samples for this study. Purposive sampling technique was used to select the sample for the study.

 

Five point Likert-like rating scale was used as tool for this study to assess the practice of Bio Medical Waste Management (BMWM) in PHCs. Video Assisted Teaching Module (VATM) on BMWM was used to teach health workers of PHCs. The data was collected from 1^st March 2009 to 31^st August 2009. The data was grouped, coded and analyzed by using descriptive statistics such as percentage, mean, SD and mean% and co-efficient of correlation. Inferential statistics was used to find the difference between pre-test and post-test Practice Scores (PS) by using ‘t’ test. A Chi-square was used to find the association between obtained post PSs with demographic variables. However, regression analysis and ANOVA were used to compare the repeated measures.

 

RESULTS:

Results of the study were presented under following headings:

Section I: Percentage-wise distribution of extraneous variables related to PHCs:

Most (84.4%) of Primary Health Centres (PHCs) had one Medical Officer (MO) and around fifty percent of PHCs had MO with 6 years and above experience and worked for the period of 1-2 years at the present PHC. Only 13% of PHCs had one Block Extension Educator (BEE) and about 33% of PHCs had 2 and above Staff Nurses. Around 60% of PHCs had one Sr. Health Assistant (F) and 42.2% of PHCs had 3-4 Jr. Health Assistant (F). Highest percentage (55.56%) of PHCs had one Health Inspector and similar percentage of PHCs also had one Jr. Health Assistant (M). Fifty six percent of PHCs were between 31-35 kms. from the city. Only 16% of MO working in PHCs had attended in-service education on BMWM, of which, 62.5% of MO attended in-service education in taluk level.

 

Section II: Assessment of practice of PHCs on Bio Medical Waste Management (BMWM).

1.      1. Assessment of existing BMWM practice in various Taluks before intervention.

 

Table-1: Assessment of Mean, SD and Mean % of BMWM PSs of PHCs in various Taluk.

 

Taluk-wise assessment of mean, SD and mean% of Practice Scores (PSs) on Bio Medical Waste Management (BMWM) before intervention shows that highest mean PS was 21.8±6.16 which is 14.34% in Devanahalli taluk and the lowest mean PS was 13.38±3.54 which is 8.8% in Doddaballapur taluk. However, the overall mean PS was 16.3±4.4 which is 10.73% of maximum PS. It reveals very poor practice on BMWM in the PHCs under all the four taluks under study before intervention (Table-1).

 

2. Comparison of pre and post IOs Mean, SD and Mean% of PSs on BMWM in various taluks.

Mean, Standard Deviation (SD) and mean% of PS on BMWM shows that during the 1^st post Intervention Observation (IO) the highest mean PS (115±8.2) which is 76.66% was for Nelamangala taluk, and the lowest mean PS (106.9±8.92) which is 70.33% was for Doddaballapur taluk. Further, the overall mean PS was 110.6±8.05 which is 72.98% of maximum PS which reveals very good practice of BMWM after the 1^st post IO. It is around 60% higher when compared to pre IO.

 

 

 

 

Table-2: Comparison of pre and post IOs Mean, SD and Mean% of BMWM PSs in various taluks.

Taluk Name	No.  of  PHC	Observations
		Pre- intervention			1st Post- intervention			2nd Post- intervention			3rd Post- intervention
		Mean	SD	Mean %	Mean	SD	Mean %	Mean	SD	Mean %	Mean	SD	Mean %
1 Nelamangala	9	16.44	3.81	10.82	115	8.2	76.66	138.8	4.52	91.34	139.3	3.91	91.64
2. Doddaballapura	16	13.38	3.54	8.8	106.9	8.92	70.33	135.4	3.46	89.1	136.8	3.23	90
3. Devanahalli	10	21.8	6.16	14.34	110.3	5.41	72.57	135.4	2.84	89.1	136.4	2.9	89.74
4. Hosakote	10	13.6	4.09	8.95	110	9.66	72.37	134.6	2.22	88.55	135.9	2.13	89.41
Over all	45	16.3	4.4	10.73	110.6	8.05	72.98	136.1	3.3	89.5	137.1	3.04	90.2

 

 

 

Further, comparison of mean, SD and mean% of PS of BMWM during the 2^nd post IO shows the highest mean PS (138.8±4.52) which is 91.34% was for Nelamangala taluk, and the lowest mean PS (134.6±2.22) which is 88.55% was for Hosakote taluk. The overall mean PS was 136.1±3.3 which is 89.5% of maximum PS.

 

However, during the 3^rd post Intervention Observation (IO) the highest mean PS (139.3±3.91) which is 91.64% was for Nelamangala taluk, and the lowest mean PS (135.9±2.13) which is 89.41%, for Hosakote taluk. The overall mean post IO PS was 137.1±3.04 which is 90.2% of maximum PS which is only around 1% or less when compared to the 2^nd observation. It is also observed that the difference between the lowest and the highest scores of the taluks were only around 5% or less in each observation (Table-2). It demonstrates additional improvement in practice on BMWM during the 2^nd and 3^rd observation when compared to the 1^st observation after the intervention.

 

Section III: Taluk-wise comparison between pre and post IOs Mean% of BMWM PS before and after intervention:

To compare the difference in the mean% of Bio Medical Waste Management (BMWM) PS between pre and each post IOs, the difference between pre and 1^st post IO, pre and 2^nd post IO, pre and 3^rd post IO, 1^st and 2^nd post IO, 2^nd and 3^rd post IO is presented in table-3.

 

 

 

Taluk-wise comparison of difference of mean percentage of BMWM PS between pre and each post Intervention Observation (IO) shows highest difference of mean percentage of PS (65.84%) for Nelamangala taluk and the lowest difference of mean percentage of PS (58.23%) for Devanahalli taluk during 1^st post IO. The difference between pre and 2^nd post IO shows the highest difference in mean% of PS was 80.52% in Nelamangala taluk, whereas the lowest difference of mean% of PS was 74.76 % in Devanahalli taluk. However, comparison of pre and 3^rd post IO shows the highest difference (81.2%) for Doddaballapur taluk and the lowest 75.4% for Devanahalli taluk.

 

Further, comparison of difference between 1^st post and 2^nd post IO reveals the highest difference (18.77%) for Doddaballapur taluk and the lowest which is 14.68% in Nelamangala taluk. The difference between 1^st post and 3^rd post IO shows highest (19.67%) for Doddaballapur taluk and the lowest (14.98%) for Nelamangala taluk and difference between 2^nd post and 3^rd post IO shows the highest difference in mean % of PS (0.9%) for Doddaballapur taluk and the lowest (0.3%) for Nelamangala taluk.

 

It reveals that the overall highest mean difference (61.87%) in PS was during 1^st post IO. However, during 1^st and 2^nd and 1^st and 3^rd the difference was only around 17% and during 2^nd and 3^rd observation it was less than 1% for various taluks. Hence, it can be interpreted that the module was practicable within one month of time (Table-3).

 

 

 

 

 

Table-3: Taluk-wise comparison of difference Mean % of BMWM PSs of PHCs between pre and each post IO.

Taluk Names	Difference of mean percentage between
	Pre and 1st Post IO	Pre and 2nd Post IO	Pre and 3rd Post IO	1st Post and 2ndPost IO	1st Post and 3rd Post IO	2ndPost and 3rdPost IO
1. Nelamangala	65.84	80.52	80.82	14.68	14.98	0.3
2.Doddaballapur	61.53	80.3	81.2	18.77	19.67	0.9
3. Devanahalli	58.23	74.76	75.4	16.53	17.17	0.64
4. Hosakote	63.42	79.6	80.46	16.18	17.04	0.86
Over all	61.87	78.95	79.78	17.08	17.91	0.83

Area-wise comparison of mean, SD and mean% of BMWM PSs of the PHCs on before and after intervention.

 

 

Table: 4:Area-wise comparison of pre and post IOs mean, SD and mean% of PSs on BMWM among the PHCs.

Areas	Max. PS	Practice Scores of PHC
		Pre- IO			1st Post-IO			2nd Post-IO			3rd Post-IO
		Mean	SD	Mean %	Mean	SD	Mean %	Mean	SD	Mean %	Mean	SD	Mean %
Generation and Segregation	20	0.42	0.78	2.11	17.2	1.88	86	19.91	0.36	99.96	19.91	0.36	99.96
Collection, Storage and Transport	76	8.29	2.57	10.91	54.27	5.33	71.4	67.98	2.48	89.44	68.58	2.47	90.23
Treatment Technologies	12	1.31	0.73	10.93	04.38	0.65	36.48	05.24	0.74	43.7	05.62	0.72	46.85
Final Disposal	36	4.36	2.13	12.1	28.27	2.99	78.52	35.42	1.03	98.4	35.42	1.03	98.4
Legal, Ethical and BMW policy	8	1.51	0.89	18.89	05.84	0.71	73.06	07.36	0.61	91.94	07.49	0.51	93.61
Over all	152	15.89	0.42	10.47	110	0.91	72.34	136	0.92	89.42	137	0.8	90

Area-wise comparison of pre and post Intervention Observations (IOs) mean PSs on various areas of practice of Bio Medical Waste Management (BMWM) during pre observation shows that out of five areas the highest mean PS was 1.51±0.89 on legal, ethical and BMW policy which is 18.89% of total PS and the lowest mean PS (0.42±0.78) which is 2.11% was for generation and segregation. Further, the total mean PS value was 15.89±0.42 which is 10.47 % of the maximum PS. It reveals very poor practice in all the areas related BMWM.

 

However, area-wise comparison of pre and post IO mean PSs of PHCs with regard to various practice areas related to BMWM shows that on the 1^st post IO, out of five areas the highest mean PS (17.2±1.88) which is 86% was for generation and segregation of BMW and the lowest mean PS (04.38±0.65) which is 36.48%, was for treatment technologies. Further, the overall mean PS value was 110±0.91 which is 72.34% of the maximum PS.

 

During the 2^nd post IO out of five areas the mean PS was the highest (19.91±0.36) for generation and segregation of BMW which is 99.96% of total PSs and the lowest mean PS (05.24±0.74), was for treatment technologies of BMW which is 43.7% of total PS. It seems that practice was excellent for all the areas related to BMWM by all PHCs except treatment technologies which were average. However, the overall mean PS value was 136±0.92 which is 89.42% of the maximum PS.

 

Further, area-wise comparison of mean PS of PHCs with regards to various practice areas related to BMWM on the 3^rd post IO depicts that out of five areas the mean PS was the highest (19.91±0.36) for generation and segregation of BMW which is 99.96% of total PS and the lowest mean PS (05.62±0.72), was for treatment technologies of BMW which is 46.85% of total PS. However, the overall mean PS value was 137±0.8 which is 90% of the maximum practice PS. The lowest improvement in treatment technologies on the 3^rd post IO might be related to financial constraints as it requires government assistance (Table-4).

 

A. Comparison of pre and post IO of BMWM PSs:

Fig-1: Line graph showing comparison of pre and post IO of BMWM PSs.

Line graph to compare the pre and post Intervention Observation (IO) of Bio Medical Waste Management (BMWM) Practice Scores (PSs) shows that the lowest score of pre test IO between 5-10 which was scored by 4% of PHCs whereas in post IO it was between 130-135 scored by 27% of PHCs. Further, the highest score of pre IO was between 25-30 obtained by 9% of PHCs. However, in post IO it was between 145-150 scored by 2% of PHCs. Highest percentage (38%) of the PHCs scored between 15-20 in pre IO, whereas in post IO it was between 135-140 scores obtained by 51% of PHCs revealing the effectiveness of biomedical waste management module on BMWM practice.

 

Further, the mean and median plotted on the graph shows that the pre IO mean and median scores were 15.91 and 15, respectively. However, in post IO the mean and median scores were 137.02 and 137, respectively and most of the post IO scores were scattered between 135-140 revealing the effectiveness of biomedical waste management module (Fig No.: 1).

 

Fig-2: O-give curve showing comparison of pre and post IO percentile of BMWM Practice Scores (PSs). 

 

O-give curve drawn to compare pre and post Intervention Observation (IO) percentile of Bio Medical Waste Management (BMWM) PSs shows that the curve of the post IO scores lies to the right of pre IO score values over the entire range, showing that the post IO scores are consistently higher than the pre test scores.

 

In the pre test 25^th quartile score was 10.1 whereas none were in the 25^th quartile score for the post IO. The 50^th quartile score for pre test was 13 which was 136 for the post IO, revealing a difference of 123 scores. Similarly the 75^th quartile score was 16 for the pre IO whereas it was 138 for the post IO, revealing a difference of 122 scores. It seems that the difference is almost similar in 50^th and 75^th quartile. It seems that the VATM was effective and the effectiveness is more or less similar throughout (Fig No.: 2).

Section-IV: Significant difference between the mean PSs of pre to post IOs and between the post IOs:

To find out the significant difference between the pre and post IOs and between the post IOs ‘t’ test were calculated.

Ho₁: There will be no significant difference between the pre test and post test PS of the waste management practices of PHCs after implementation of the VATM.

 

Paired ‘t’ test was calculated to find out the significant difference between pre and 1^st, 2^nd, 3^rd post IOs of BMWM PSs of PHCs. Highly significance difference was found between pre and 1^st, 2^nd, 3^rd post IOs PSs. Similarly highly significance difference was found between 1^st and 2^nd and, 2^nd and 3^rd post IO PSs. However, significance difference was found between 2^nd and 3^rd post IOs of BMWM PSs.

 

Hence, the null hypothesis is rejected and concluded that there is significant difference between pre and 1^st, 2^nd, 3^rd post IO of BMWM PSs of PHCs. Thus, the difference observed in the mean PS values of pre and post IO PS were true differences and not by chance (Table-5). It seems that the VATM on BMWM was effective and effectiveness was increased with increase in days after intervention.

 

Section V: Association between the PSs of PHCs on BMWM with their extraneous variables after the implementation of VATM:

To find out the association between the post interventions PSs of BMWM of the extraneous variables under study hypotheses were formulated and chi-squire was calculated.

HO₂: There will be no significant difference between the post test PSs of the waste management practices of PHCs after implementation of the VATM with the selected extraneous variables.

 

 

 

 

 

 

 

 

Table-5: Paired ‘t’ test of pre and post 1^st, 2^nd and 3^rd IOs and between the post IOs of BMWM PSs of PHCs.

Interventions	df	‘t’- value	p- value	Level of Significance
Pre and post-1	44	80.42	p < 0.001	Highly Significant
Pre and post-2	44	136.69	p < 0.001	Highly Significant
Pre and post-3	44	137.22	p < 0.001	Highly Significant
Post-1 and post-2	44	137.22	p < 0.001	Highly Significant
Post-1 and post-3	44	27.22	p < 0.001	Highly Significant
Post-2 and post-3	44	07.13	p < 0.05	Significant

 

Table-6: Association between the PSs of the PHCs with the extraneous variables on post IOs.

Demographic Variables	d.f	Table Value	Post Observation -1			Post Observation -2			Post Observation -3
			Chi-Square	p-value	LoS	Chi-Square	p-value	LoS	Chi-Square	p-value	LoS
Number of MOs working.	1	2.71	0.01	0.95	NS	0.01	0.902	NS	0.01	0.99	NS
Years of Experience of MOs (total).	1	2.71	0.58	0.44	NS	0.11	0.75	NS	0.27	0.61	NS
Duration of service of MOs in sample PHC	2	4.61	1.28	0.53	NS	4.48	0.18	NS	8.98	p < 0.01	S
Number of BEE working.	1	2.71	0.48	0.26	NS	0.15	0.70	NS	0.10	0.75	NS
Number of Staff Nurses working.	1	2.71	0.18	0.67	NS	1.00	0.32	NS	0.38	0.54	NS
Number of LHVs working.	1	2.71	1.91	0.17	NS	0.54	0.46	NS	3.02	0.08	NS
Number of Jr. Health    Asst. (F) working	1	2.71	2.74	0.10	NS	2.67	0.10	NS	1.58	0.21	NS
Number of Health Inspectors.	1	2.71	1.92	0.19	NS	0.56	0.48	NS	3.03	0.08	NS
Number of Jr. Health   Asst. (M) working	2	2.71	0.54	0.46	NS	0.64	0.73	NS	1.31	0.52	NS
Distance of PHC from City.	1	2.71	1.78	0.18	NS	0.34	0.56	NS	1.40	0.24	NS
Duration of training provided on BMWM.	1	2.71	0.01	0.95	NS	0.53	0.47	NS	0.01	0.99	NS
Place of training provided on BMWM.	1	2.71	0.01	0.94	NS	1.41	0.24	NS	0.08	0.78	NS
Number of health workers in PHC	2	2.71	0.29	0.23	NS	0.3	0.86	NS	0.28	0.87	NS

LoS=Level of Significance,  NS= Not Significant, S= Significant, BMWM= BMWM, PHC= Primary Health Center.

 

 

 

No significant association was found between 1^st, 2^nd and 3^rd post intervention PSs when compared to Number of Medical officers (MOs), Years of Experience of MO (total), Duration of service in the sample PHC, number of BEE, number of Staff Nurses, number of Lady Health Visitors, number of Health Asst. (F), Number of Health Inspectors, number of Health Asst. (M), distance of PHCs from city, duration of training provided on BMWM, Place of training provided on Bio Medical Waste Management (BMWM) and number of health workers in PHC (p>0.01). Hence, it can be interpreted that the difference in mean PS values related to the above extraneous variables were only by chance and not the true difference and the null hypothesis is accepted. However, significant association was found between 3^rd post observation PS value when compared to duration of service of MO in the sample PHC (p<0.01).

 

Hence, it can be interpreted that the difference in post IO mean PS values related to distance of PHC from city and practice were true and the null hypothesis is rejected. It might be that those PHCs which were above 26 kilometres were more interested as they are not able to use facilities of the city (Table-6). Hence, it can be interpreted that the VATM was highly effective for all the PHCs under study. However, the effectiveness increases with distance of PHC from the city.

 

Section VI: Comparison of PS between Scores by using repeated measures (ANOVA) among PHCs on BMWM.

 

To compare the PSs of repeated observations the ANOVA was calculated. The findings are as follows: 

 

Table-7: Comparison of PSs by using repeated measures among PHC on BMWM.

              

Comparison of pre and 1^st, 2^nd and 3^rd post Intervention Observation (IO) Practice Score (PSs) by using repeated measures ANOVA was computed among Primary Health Centres (PHCs) on BMWM finding reveal statistically significant difference between the IOs (F = 8268) (Table-7).

 

Thus, the difference observed in the mean PS values of pre and post IOs PSs were true differences.

 

CONCLUSION:

From the findings of the present study, it can be concluded that VATM was effective to improve the practice of BMWM in Primary Health Centres (PHCs).

 

 

 

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Received on 24.01.2014           Modified on 15.03.2014

Accepted on 30.03.2014           © A&V Publication all right reserved