| Literature DB >> 23497151 |
Arwa Alumran1, Xiang-Yu Hou, Cameron Hurst.
Abstract
BACKGROUND: Antibiotics overuse is a global public health issue influenced by several factors, of which some are parent-related psychosocial factors that can only be measured using valid and reliable psychosocial measurement instruments. The PAPA scale was developed to measure these factors and the content validity of this instrument was assessed. AIM: This study further validated the recently developed instrument in terms of (1) face validity and (2) construct validity including: deciding the number and nature of factors, and item selection.Entities:
Mesh:
Substances:
Year: 2013 PMID: 23497151 PMCID: PMC3600044 DOI: 10.1186/1477-7525-11-39
Source DB: PubMed Journal: Health Qual Life Outcomes ISSN: 1477-7525 Impact factor: 3.186
Demographic characteristics of study participants
| | | | |
| 20 – 30 | 22 (13.2) | 0 (0) | 22 (9.3) |
| 31 – 40 | 86 (51.5) | 20 (28.6) | 106 (44.7) |
| 41 – 50 | 36 (21.6) | 31 (44.3) | 67 (28.3) |
| >50 | 0 (0) | 15 (21.4) | 15 (6.3) |
| | 23 (13.8) | 4 (5.7) | 27 (11.4) |
| | | | |
| 1 child | 43 (26.1) | 20 (8.6) | 63 (27.0) |
| 2 children | 49 (29.7) | 19 (8.2) | 68 (29.2) |
| 3 children | 57 (34.5) | 17 (7.3) | 74 (31.8) |
| 4 children | 11 (6.7) | 8 (3.4) | 19 (8.2) |
| 5 children | 5 (3.0) | 4 (1.7) | 9 (3.9) |
| | 2 (1.2) | 2 (2.9) | 4 (1.7) |
| | | | |
| Illiterate | 2 (1.2) | 0 (0) | 2 (0.9) |
| No formal education | 2 (1.2) | 0 (0) | 2 (0.9) |
| Junior high school | 7 (4.2) | 7 (10.1) | 14 (6.0) |
| High school | 26 (15.8) | 6 (8.7) | 32 (13.7) |
| Diploma or bachelor | 125 (75.8) | 38 (55.1) | 163 (13.7) |
| Higher degrees | 3 (1.8) | 18 (26.1) | 21 (9.0) |
| | 2 (1.2) | 1 (1.4) | 3 (1.3) |
| | | | |
| Unemployed | 3 (1.8) | 2 (2.9) | 5 (2.1) |
| Employed | 91 (55.5) | 55 (78.6) | 146 (62.4) |
| Student | 6 (3.7) | 0 (0) | 6 (2.6) |
| Housewife | 56 (34.1) | 0 (0) | 56 (23.9) |
| Self-employed | 5 (3.0) | 7 (10.0) | 12 (5.1) |
| Retired | 3 (1.8) | 6 (8.6) | 9 (3.8) |
| | 3 (1.8) | 0 (0) | 3 (1.3) |
| | | | |
| Low | 12 (8.0) | 9 (13.8) | 21 (9.8) |
| Low middle | 53 (35.3) | 20 (30.8) | 73 (34.0) |
| Middle | 54 (36.0) | 16 (24.6) | 70 (32.6) |
| High middle | 19 (12.7) | 12 (18.5) | 31 (14.4) |
| High | 12 (8.0) | 8 (12.3) | 20 (9.3) |
| | 17 (10.2) | 5 (7.1) | 22 (9.3) |
| | | | |
| Yes | 27 (16.5) | 10 (14.7) | 37 (15.9) |
| No | 137 (83.5) | 58 (85.3) | 195 (84.1) |
| | 3 (1.8) | 2 (2.9) | 5 (2.1) |
| | | | |
| Eastern region | 91 (59.5) | 34 (55.7) | 125 (58.4) |
| Western region | 13 (8.5) | 7 (11.5) | 20 (9.3) |
| Middle region | 23 (15.0) | 10 (16.4) | 33 (15.4) |
| Northern region | 2 (1.3) | 3 (4.9) | 5 (2.3) |
| Southern region | 24 (15.7) | 7 (11.5) | 31 (14.5) |
| | 14 (8.4) | 9 (12.9) | 23 (9.7) |
*Children less than 12 years old.
** Income: low:
Children’s health-related history according to their parents
| Never | 0 | 0 | 0 | 0 | |
| Once | 7 | 12 | 2 | 0 | |
| 2–3 times | 13 | 24 | 9 | 1 | |
| 4–6 times | 2 | 6 | 12 | 4 | |
| > 6 times | 0 | 0 | 3 | 2 | |
Total = 235 (3 missing values).
Figure 1Parallel Analysis derived from a principal component analysis.
The pattern coefficients from a principal axis factoring using promax rotation
| | |
| .627 | |
| .631 | |
| .461 | |
| .574 | |
| .594 | |
| .687 | |
| .749 | |
| .628 | |
| .613 | |
| .672 | |
| | |
| 443 | |
| .426 | |
| .809 | |
| .534 | |
| .874 | |
| .623 | |
| | |
| .479 | |
| .472 | |
| .759 | |
| .621 | |
| .773 | |
| .789 | |
| .556 | |
| | |
| .465 | |
| .716 | |
| .902 | |
| .517 | |
| .697 | |
| | |
| .446 | |
| -.422 | |
| .444 | |
| .446 | |
| .674 | |
| | |
| .441 | |
| .622 | |
| .674 | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
| | |
The likely construct associated with each factor is also included.
Inter-factor correlation matrix
| 1.000 | .038 | -.058 | .171 | .010 | -.078 | |
| .038 | 1.000 | .113 | .535 | -.403 | -.098 | |
| -.058 | .113 | 1.000 | .127 | -.189 | .192 | |
| .171 | .127 | 1.000 | -.473 | .104 | ||
| .010 | -.189 | 1.000 | -.017 | |||
| -.078 | -.098 | .192 | .104 | -.017 | 1.000 |
Extraction method: principal axis factoring.
Rotation method: promax rotation with Kaiser normalization.
Figure 2The frequency distribution of the dimensions mentioned in the literature.
Figure 3Conceptual framework.