Table 1 Participant characteristics by sex and check-up location

From: Implementation and evaluation of the Y-Check comprehensive adolescent health check-up intervention in Zimbabwe: a pre−post mixed-methods study

  

Male (n = 912)

Female (n = 1,185)

Primary schools (n = 1,071)

Secondary schools (n = 387)

Community venues (n = 639)

Age in years

10–11

368 (40.4%)

476 (40.2%)

844 (78.8%)

  

12–13

115 (12.6%)

105 (8.9%)

220 (20.5%)

  

14–15

14 (1.5%)

56 (4.7%)

7 (0.7%)

63 (16.3%)

 

16–17

378 (41.4%)

503 (42.4%)

 

294 (76.0%)

587 (91.9%)

18–19

37 (4.1%)

45 (3.8%)

 

30 (7.7%)

52 (8.1%)

Sex

Male

  

485 (45.3%)

140 (36.2%)

287 (44.9%)

 

Female

  

586 (54.7%)

247 (63.8%)

352 (55.1%)

Location

School A

128 (14.0%)

198 (16.7%)

326 (30.4%)

  
 

School B

134 (14.7%)

151 (12.7%)

285 (26.6%)

  
 

School C

83 (9.1%)

83 (7.0%)

166 (15.5%)

  
 

School D

140 (15.4%)

154 (13.0%)

294 (27.5%)

  
 

School E

84 (9.2%)

150 (12.7%)

 

234 (60.5%)

 
 

School F

56 (6.1%)

97 (8.2%)

 

153 (39.5%)

 
 

Community 1

158 (17.3%)

147 (12.4%)

  

305 (47.7%)

 

Community 2

129 (14.1%)

205 (17.3%)

  

334 (52.3%)

Who the participant lives with (multiple selection)

Parents +/− other relatives

794 (87.1%)

981 (82.8%)

970 (90.6%)

315 (81.4%)

490 (76.7%)

Other relatives (not parents)

111 (12.2)

196 (16.5%)

100 (9.3%)

70 (18.1%)

137 (21.4%)

Partner/spouse (not relatives)

0 (0%)

6 (0.5%)

1 (0.1%)

0 (0%)

5 (0.8%)

Lives alone

7 (0.8%)

2 (0.2%)

0 (0%)

2 (0.5%)

7 (1.1%)

Guardian working status

Working for someone

(self-employed and employed)

757 (83.4%)

942 (80.0%)

878 (82.6%)

325 (84.2%)

496 (77.9%)

Unemployed

(seeking and not seeking work)

90 (9.9%)

177 (15.0%)

119 (11.2%)

42 (10.9%)

106 (16.6%)

Retired

21 (2.3%)

27 (2.3%)

20 (1.9%)

13 (3.4%)

15 (2.4%)

Unknown (do not know/do not want to answer)

40 (4.4%)

32 (2.7%)

46 (4.3%)

6 (1.5%)

20 (3.1%)

Missing

4

7

8

1

2

Asset index

1st (lowest quintile)

135 (17.3%)

225 (22.2%)

162 (18.7%)

65 (18.5%)

133 (23.1%)

2nd

147 (18.8%)

216 (21.3%)

165 (19.0%)

78 (22.2%)

120 (20.9%)

3rd

151 (19.4%)

220 (21.7%)

169 (19.5%)

84 (23.9%)

118 (20.5%)

4th

173 (22.2%)

187 (18.4%)

181 (20.9%)

69 (19.7%)

110 (19.1%)

5th (highest quintile)

173 (22.2%)

166 (16.4%)

190 (21.9%)

55 (15.7%)

94 (16.4%)

Excluded

129

164

196

35

62

Missing

4

7

8

1

2

Religion

Muslim

28 (3.1%)

42 (3.6%)

60 (5.6%)

3 (0.8%)

7 (1.1%)

Christian

782 (86.1%)

1036 (87.9%)

888 (83.5%)

363 (94.0%)

567 (89.0%)

African traditional

26 (2.9%)

27 (2.3%)

30 (2.8%)

7 (1.8%)

16 (2.5%)

None

28 (3.1%)

34 (2.9%)

32 (3.0%)

8 (2.1%)

22 (3.5%)

Do not know

29 (3.2%)

24 (2.0%)

39 (3.7%)

2 (0.5%)

12 (1.9%)

Other

15 (1.7%)

15 (1.3%)

14 (1.3%)

3 (0.8%)

13 (2.0%)

Missing

4

7

8

1

2

  1. The assets: refrigerator, bicycle, animal-drawn cart, motorcycle, boat, cart, television, radio, microwave, cell phone and computer. We excluded everyone who said they owned either zero assets (n = 108) or all 11 assets (n = 185), because the data presented ceiling and floor effects distribution, which is unlikely given the socioeconomic context of the study locations. We performed a factor analysis for data reduction and generation of the asset index. Each asset gets a ‘weight’ (factor loading), and patterns of asset ownership predict the asset index score. The scores are grouped into quintiles.
Back to article page