| Literature DB >> 33238994 |
Yaping Xu1,2,3, Wei Wang4, Kaiyuan Zhen5, Jing Zhao4.
Abstract
BACKGROUND: The accurate identification of venous thromboembolism prophylaxis implementation barriers is an important part of prophylaxis prevention. However, in China, data to help identify these barriers is limited. This study has two objectives: 1) to determine the knowledge, attitudes, and practices (KAPs) of healthcare professionals regarding graduated compression stockings (GCS) since the launch of the National Program for the Prevention and Management of Pulmonary Embolism (PE) and Deep Venous Thrombosis (DVT) in October 2018 and 2) to identify the obstacles and assist the program.Entities:
Keywords: Attitude; Graduated compression stockings; Knowledge; Practice; Venous thromboembolism
Mesh:
Year: 2020 PMID: 33238994 PMCID: PMC7690181 DOI: 10.1186/s12913-020-05933-9
Source DB: PubMed Journal: BMC Health Serv Res ISSN: 1472-6963 Impact factor: 2.655
Respondent characteristics (N = 5070)
| Characteristic | Categories | % | |
|---|---|---|---|
| Gender | Men | 454 | 9.0 |
| Women | 4616 | 91.0 | |
| Age (years) | < 29 | 1999 | 39.4 |
| 30–39 | 2180 | 43.0 | |
| ≥ 40 | 891 | 17.6 | |
| Highest education attained | Secondary a | 67 | 1.3 |
| College | 921 | 18.2 | |
| Bachelor’s degree | 3659 | 72.2 | |
| Master’s degree | 382 | 7.5 | |
| Doctoral degree | 41 | 0.8 | |
| Profession | Doctor | 654 | 12.9 |
| Nurse | 4416 | 87.1 | |
| Hospital level | Tertiary hospital | 4523 | 89.2 |
| Secondary hospital | 547 | 10.8 | |
| Service years | 1–5 | 1495 | 29.5 |
| 6–10 | 1501 | 29.6 | |
| 11–19 | 1271 | 25.1 | |
| ≥ 20 | 803 | 15.8 | |
| Professional title | Junior | 2992 | 59.0 |
| Intermediate | 1688 | 33.3 | |
| Senior | 390 | 7.7 | |
| Administrative duties | No | 4422 | 87.2 |
| Education secretary | 337 | 6.7 | |
| Head nurse | 205 | 4.0 | |
| Doctor director | 106 | 2.1 | |
| GCS application training | Yes | 3216 | 63.4 |
| No b | 1854 | 36.6 |
GCS Graduated compression stockings
aEven in some tertiary hospitals in China, there are a few nurses who have worked for more than 30 years who have only completed secondary education
bIdeally, all healthcare professionals in hospitals accredited by the National Program for Prevention and Management of primary embolism and deep vein thrombosis should have training for the use of GCS; however, the survey shows a wide gap in the implementation of the program in the real world
Fig. 1Proportion of “good” classifications for KAP dimensions (N = 5070)
Fig. 2Proportion of “good” classifications for the 22 KAP dimension items (N = 5070). Knowledge (11 items) 1. Mechanism of action; 2. Indications; .3. Contraindications; 4. Size; 5. Pressure level; 6. Length; 7. Timing; 8. Wearing method; 9. Maintenance instructions; 10. Washing method; and 11. Service life. Attitude (4 items) 1. GCS benefits should be actively communicated to patients and their caregivers; 2. Medical staff should teach patients and their caregivers the proper use of GCS; 3. Medical institutions and managers should pay attention to the training of healthcare professionals for GCS usage; and 4. GCS should be covered by Medicare. Practice (7 items) 1. I think my guidance on GCS usage for patients is in place; 2. I make sure that the patients in my charge have been well informed of GCS usage benefits; 3. I make sure that the patients in my charge are well aware of the importance of the proper use of GCS upon discharge; 4. I make sure that the patients in my charge are capable of wearing GCS independently or with the help of the caregivers when they leave the hospital; 5. I make sure that the patients under my charge know how to solve problems (such as skin indentation, blisters or discoloration) associated with GCS usage, especially at the ankle or protuberance, after discharge; 6. I make sure that at discharge I have made clear to the patients in my charge who to contact in case of GCS misusage; and 7. I make sure that at discharge I have made it clear to the patients in my charge when to stop using GCS
KAP means, standard deviations, and Pearson’ s correlation coefficients (N = 5070)
| Variables | Minimum | Maximum | Knowledge | Attitude | Behavior | ||
|---|---|---|---|---|---|---|---|
| Knowledge | 38.18 | 9.098 | 11 | 55 | 1 | ||
| Attitude | 16.65 | 2.656 | 4 | 20 | 0.463* | 1 | |
| Behavior | 22.22 | 8.227 | 7 | 35 | 0.658* | 0.428* | 1 |
The correlation is significant at 0.01 (two-tailed)
M Mean, SD Standard deviation
*p < 0.01
Single-factor comparison of the demographic characteristics that influence “good” and “not good” classifications in the knowledge dimension
| Good ( | Not good ( | |||
|---|---|---|---|---|
| Gender, Men (%) | 167 (10.1) | 287 (8.4) | 4.202a | 0.040* |
| Age (years) | 26.331a | 0.000** | ||
| <29 | 733 (44.5) | 1266 (37.0) | ||
| 30–39 | 649 (39.4) | 1531 (44.7) | ||
| ≥ 40 | 265 (16.1) | 626 (18.3) | ||
| Highest education attained | 5.955a | 0.203 | ||
| Secondary | 20 (1.2) | 47 (1.4) | ||
| College | 279 (16.9) | 642 (18.8) | ||
| Bachelor’s degree | 1217 (73.9) | 2442 (71.3) | ||
| Master’s degree | 114 (6.9) | 268 (7.8) | ||
| Doctoral degree | 17 (1.0) | 24 (0.7) | ||
| Profession | 0.159a | 0.690 | ||
| Doctor | 208 (12.6) | 446 (13.0) | ||
| Nurse | 1439 (87.4) | 2977 (87.0) | ||
| Hospital level | 4.649a | 0.031* | ||
| Tertiary hospital | 1447 (87.9) | 3076 (89.9) | ||
| Secondary hospital | 200 (12.1) | 347 (10.1) | ||
| Service years | 16.063a | 0.001** | ||
| 1–5 | 542 (32.9) | 953 (27.8) | ||
| 6–10 | 482 (29.3) | 1019 (29.8) | ||
| 11–19 | 390 (23.7) | 881 (25.7) | ||
| ≥ 20 | 233 (14.1) | 570 (16.7) | ||
| Professional title | 2.001a | 0.368 | ||
| Junior | 995 (60.4) | 1997 (58.3) | ||
| Intermediate | 531 (32.2) | 1157 (33.8) | ||
| Senior | 121 (7.3) | 269 (7.9) | ||
| Administrative duties | 12.664a | 0.005** | ||
| No | 1420 (86.2) | 3002 (87.7) | ||
| Education | ||||
| Secretary | 130 (7.9) | 207 (6.0) | ||
| Head nurse | 54 (3.3) | 151 (4.4) | ||
| Doctor director | 43 (2.6) | 63 (1.8) | ||
| GCS application training | 810.688a | 0.000** | ||
| Yes | 1502 (91.2) | 1714 (50.1) | ||
| No | 145 (8.8) | 1709 (49.9) | ||
aPearson Chi-Square test
*p < 0.05; **p < 0.01
The factors that influence “good” and “not good” classifications in the knowledge dimension in binary logistic regression analysis
| 95%CI (Lower bound/upper bound) | ||
|---|---|---|
| Gender | 0.038 | |
| Men | 1 | * |
| Women | 0.772 (0.605–0.986) | |
| Age (years) | 0.110 | |
| <29 | 1 | |
| 30–39 | 0.794 (0.639–0.985) | |
| ≥ 40 | 0.764 (0.487–1.198) | |
| Hospital level | 0.684 | |
| Tertiary hospital | 1 | |
| Secondary hospital | 0.958 (0.781–1.176) | |
| Service years | 0.668 | |
| 1–5 | 1 | |
| 6–10 | 0.983 (0.805–1.201) | |
| 11–19 | 0.958 (0.730–1.256) | |
| ≥ 20 | 0.748 (0.462–1.211) | |
| Administrative duties | 0.050 | |
| No | 1 | |
| Education secretary | 1.253 (0.962–1.633) | |
| Head nurse | 0.806 (0.570–1.140) | |
| Doctor director | 1.602 (0.992–2.586) | |
| GCS application training | 0.000** | |
| Yes | 1 | |
| No | 0.097 (0.081–0.117) |
CI Confidence interval
*p < 0.05; **p < 0.01