Table 1
Descriptive statistics for BASiC-QI scale items
|
Mean (SD) | |||||
|---|---|---|---|---|---|
|
PRE |
POST |
(SD) |
p-value |
95% CI | |
|
Subscale 1: Attitudes and Beliefs |
50.0 (6.59) |
54.8 (6.71) |
4.77 (7.21) |
0.000*** |
2.91, 6.63 |
|
1. I enjoy QI |
4.54 (0.850) |
5.63 (1.03) |
1.092 (1.10) |
0.000* |
0.809, 1.375 |
|
2. I am interested in QI |
5.64 (0.898) |
5.83 (1.03) |
0.190 (1.11) |
0.191 |
−0.097, 0.477 |
|
3. I understand the role QI plays in the health system |
5.34 (1.17) |
6.20 (0.605) |
0.862 (1.21) |
0.000* |
0.548, 1.175 |
|
4. QI plays an important role in strengthening systems, such as healthcare |
5.87 (0.911) |
6.30 (0.743) |
0.435 (1.01) |
0.002* |
0.173, 0.697 |
|
5. I value QI training as part of my professional development |
5.71 (0.884) |
6.12 (0.904) |
0.405 (1.04) |
0.004* |
0.135, 0.675 |
|
6. I want to participate in QI initiatives as a health professional |
5.71 (0.884) |
6.00 (0.957) |
0.288 (1.11) |
0.048 |
0.003, 0.574 |
|
7. Applications of QI theory and methodologies can help make change to a system |
5.81 (0.892) |
6.15 (1.04) |
0.337 (1.13) |
0.024 |
0.045, 0.628 |
|
8. Using QI in the real world will make improvements |
5.80 (0.879) |
6.32 (0.676) |
0.520 (0.910) |
0.000* |
0.285, 0.755 |
|
9. I understand the rationale for QI in the real world |
5.61 (1.12) |
6.23 (0.795) |
0.640 (1.18) |
0.000* |
0.336, 0.944 |
|
Subscale 2: Knowledge of QI |
25.7 (11.1) |
49.4 (7.41) |
23.7 (10.2) |
0.000*** |
21.1, 26.4 |
|
1. QI theory |
2.64 (1.40) |
5.22 (1.14) |
2.58 (1.51) |
0.000* |
2.99, 12.7 |
|
2. How QI is different than research |
3.26 (1.68) |
5.48 (1.13) |
2.23 (1.63) |
0.000* |
2.65, 10.6 |
|
3. Systems thinking |
2.98 (1.56) |
5.08 (1.21) |
2.10 (1.53) |
0.000* |
2.50, 10.7 |
|
4. 6 dimensions of quality |
2.43 (1.44) |
5.73 (1.18) |
3.30 (1.64) |
0.000* |
3.73, 15.6 |
|
5. Understanding processes within a system |
3.00 (1.62) |
5.30 (1.23) |
2.30 (1.49) |
0.000* |
2.69, 12.0 |
|
6. The Model for Improvement |
2.50 (1.38) |
5.27 (1.18) |
2.77 (1.29) |
0.000* |
3.10, 16.6 |
|
7. PDSA cycles |
2.15 (1.34) |
5.83 (1.04) |
3.68 (1.57) |
0.000* |
4.08, 18.2 |
|
8. How to measure the impact of a change |
3.27 (1.53) |
5.70 (0.850) |
2.43 (1.51) |
0.000* |
2.82, 12.5 |
|
9. How change links to improvement |
3.48 (1.54) |
5.82 (0.701) |
2.33 (1.49) |
0.000* |
2.72, 12.1 |
|
Subscale 3: QI Skills |
27.8 (10.3) |
53.3 (14.3) |
25.5 (13.5) |
0.000*** |
22.0, 29.0 |
|
1. Understanding quality gaps |
2.68 (1.12) |
4.42 (1.20) |
1.73 (1.17) |
0.000** |
2.04, 11.5 |
|
2. Identifying quality gaps |
2.81 (1.10) |
4.72 (1.26) |
1.91 (1.38) |
0.000** |
2.27, 10.7 |
|
3. Approach quality improvement projects |
2.12 (1.09) |
4.30 (1.42) |
2.18 (1.38) |
0.000** |
2.53, 12.2 |
|
4. Understand root causes of quality gaps |
2.25 (0.962) |
4.13 (1.36) |
1.89 (1.29) |
0.000** |
2.22, 11.4 |
|
5. Identifying an area for improvement |
3.00 (1.11) |
4.70 (1.21) |
1.70 (1.27) |
0.000** |
2.03, 10.4 |
|
6. Application of evidence and best practices to the real world |
2.81 (1.17) |
4.32 (1.46) |
1.51 (1.57) |
0.000** |
1.91, 7.47 |
|
7. Writing an aim statement |
2.12 (0.975) |
4.57 (1.43) |
2.44 (1.38) |
0.000* |
2.80, 13.7 |
|
8. Using tools to identify areas for improvement |
2.09 (1.03) |
4.45 (1.33) |
2.36 (1.33) |
0.000** |
2.71, 13.8 |
|
9. Using the Model for Improvement |
1.77 (0.939) |
4.25 (1.42) |
2.48 (1.39) |
0.000** |
2.84, 13.9 |
|
10. Using PDSA cycles to plan and test a change |
1.49 (0.866) |
4.67 (1.28) |
3.18 (1.35) |
0.000** |
3.53, 18.2 |
|
11. Designing an intervention or change |
2.44 (1.06) |
4.47 (1.41) |
2.03 (1.46) |
0.000** |
2.41, 10.8 |
|
12. Use a family of measures to evaluate the impact of a change |
2.18 (1.18) |
4.28 (1.39) |
2.11 (1.55) |
0.000** |
2.51, 10.5 |
|
TOTAL SCORE |
103.5 (24.4) |
157.5 (25.1) |
54.99 (25.5) |
0.000*** |
47.4, 60.6 |
*statistical significance at p < 0.005 level; **statistical significant at p < 0.004 level; ***statistical significant at p < 0.0125 level; Bonferroni corrections used to correct for multiple comparisons
Table 2
Exploratory factor analysis
|
Factor loadings | |||
|---|---|---|---|
|
1 |
2 |
3 | |
|
Subscale 1: Attitudes and Beliefs | |||
|
1. I enjoy QI |
0.212 |
0.644 |
0.101 |
|
2. I am interested in QI |
0.110 |
0.795 |
– |
|
3. I understand the role QI plays in the health system |
0.189 |
0.703 |
– |
|
4. QI plays an important role in strengthening systems, such as healthcare |
– |
0.842 |
– |
|
5. I value QI training as part of my professional development |
−0.140 |
0.971 |
– |
|
6. I want to participate in QI initiatives as a health professional |
– |
0.744 |
0.124 |
|
7. Applications of QI theory and methodologies can help make change to a system |
– |
0.931 |
– |
|
8. Using QI in the real world will make improvements |
– |
0.878 |
– |
|
9. I understand the rationale for QI in the real world |
– |
0.973 |
– |
|
Subscale 2: Knowledge of QI | |||
|
1. QI theory |
– |
0.108 |
0.798 |
|
2. How QI is different than research |
– |
– |
0.475 |
|
3. Systems thinking |
−0.124 |
−0.210 |
0.997 |
|
4. 6 dimensions of quality |
– |
0.124 |
0.676 |
|
5. Understanding processes within a system |
– |
– |
0.812 |
|
6. The Model for Improvement |
0.148 |
0.148 |
0.601 |
|
7. PDSA cycles |
– |
– |
0.714 |
|
8. How to measure the impact of a change |
0.484 |
– |
0.133 |
|
9. How change links to improvement |
0.280 |
0.437 |
0.213 |
|
Subscale 3: QI Skills | |||
|
1. Understanding quality gaps |
0.934 |
– |
– |
|
2. Identifying quality gaps |
0.955 |
– |
−0.168 |
|
3. Approach quality improvement projects |
0.842 |
– |
– |
|
4. Understand root causes of quality gaps |
0.693 |
– |
0.226 |
|
5. Identifying an area for improvement |
0.937 |
– |
−0.192 |
|
6. Application of evidence and best practices to the real world |
0.743 |
– |
0.212 |
|
7. Writing an aim statement |
0.945 |
– |
−0.165 |
|
8. Using tools to identify areas for improvement |
0.921 |
– |
– |
|
9. Using the Model for Improvement |
0.935 |
– |
0.114 |
|
10. Using PDSA cycles to plan and test a change |
0.894 |
– |
– |
|
11. Designing an intervention or change |
0.878 |
– |
– |
|
12. Use a family of measures to evaluate the impact of a change |
0.783 |
– |
0.140 |
|
Proportion of variance |
0.323 |
0.224 |
0.140 |
|
Variance component % |
32.3% |
22.4% |
14.0% |
Extraction method: Maximum likelihood estimation with promax oblique minimum rotation
Table 3
Generalizability ANOVA table (φ = 0.605)
|
Source |
Df |
SS |
MS |
Variance component |
% Variance |
|---|---|---|---|---|---|
|
S |
59 |
1,237.23 |
20.9701 |
0.575 |
27.4 |
|
D |
2 |
885.485 |
442.743 |
0.734 |
35.0 |
|
I:D |
27 |
88.5815 |
3.28080 |
0.048 |
2.29 |
|
S|D |
118 |
428.948 |
3.63515 |
0.325 |
15.5 |
|
S|I:D |
1,592 |
659.252 |
0.41384 |
0.414 |
19.8 |
= 0.605
σ 2 variance component
S student, D subscale, I item, Df degrees of freedom, SS sums of squares, MS mean square
Table 4
Decision study with post-PRIME data (reliability across different levels)
|
Subscales |
Items |
Total items |
σ2 (τ) |
σ2 (δ) |
σ2 (∆) |
Absolute error φ |
Relative error Ep2 |
|---|---|---|---|---|---|---|---|
|
Original scale |
30 |
0.575 |
0.124 |
0.376 |
0.605 |
0.822 | |
|
3 |
5 |
15 |
0.575 |
0.139 |
0.392 |
0.595 |
0.805 |
|
3 |
20 |
60 |
0.575 |
0.118 |
0.368 |
0.609 |
0.830 |
|
1 |
5 |
5 |
0.575 |
0.408 |
1.152 |
0.333 |
0.585 |
|
1 |
10 |
10 |
0.575 |
0.367 |
1.106 |
0.342 |
0.610 |
σ 2 variance component, τ error term, δ signal term, ∆ interactions and main effects
Table 5
QIKAT scores
|
Mean (SD) | |||||
|---|---|---|---|---|---|
|
QIKAT-R scenario |
Pre (T = 1) |
Post (T = 2) |
∆ |
95% CI |
p-value |
|
Aim |
3.60 (2.02) |
6.88 (1.98) |
3.28 (2.82) |
2.55, 4.00 |
<0.000* |
|
Measure |
4.83 (2.17) |
7.23 (1.67) |
2.40 (2.50) |
1.75, 3.05 |
<0.000* |
|
Change |
3.19 (1.74) |
4.93 (1.83) |
1.74 (2.10) |
1.20, 2.28 |
<0.000* |
|
Total QIKAT-R (/27) |
11.6 (5.01) |
19.0 (4.17) |
7.39 (6.12) |
5.81, 8.71 |
<0.000* |
* statistical significance at 0.0125 level; Bonferroni correction for multiple comparisons