| Literature DB >> 32746903 |
Kolsoum Deldar1, Razieh Froutan2,3, Alireza Sedaghat4, Seyed Reza Mazlom5,6.
Abstract
BACKGROUND: Nursing staff training in using observational pain assessment tools is highly important to improve the assessment of pain. The present study was conducted to examine the effect of two different training methods (lectures vs. a social networking app) on the diagnosis and management of pain in mechanically-ventilated patients.Entities:
Keywords: Critical-care pain observation tool; Education; Intensive care unit; Lectures; Nurse; Social networking app
Mesh:
Year: 2020 PMID: 32746903 PMCID: PMC7396891 DOI: 10.1186/s12909-020-02159-5
Source DB: PubMed Journal: BMC Med Educ ISSN: 1472-6920 Impact factor: 2.463
Nurse’ performance checklist (based on CPOT)
| A. Pain Diagnosis | Yes / No | |
|---|---|---|
| Nurse can detect the pain indicators from facial expressions of patient, correctly. For example: presence of frowning, brow lowering, orbit tightening and levator contraction or any other change (e.g. opening eyes or tearing during nociceptive procedures). | ||
| Nurse can detect the pain indicators from body movements of patient, correctly. For example: Slow, cautious movements, touching or rubbing the pain site, seeking attention through movements, pulling tube, attempting to sit up, moving limbs/thrashing, not following commands, striking at staff, trying to climb out of bed | ||
| Nurse can detect the pain indicators of patient’s compliance with the ventilator, correctly. For example: coughing, blocking ventilation, frequently activated alarms. | ||
| Nurse can detect the pain indicators from muscle tension of patient, correctly. For example: Strong resistance to passive movements or incapacity to complete them. | ||
| Total Score: | Mean Score: | Corrected Score (Mean %): |
| Nurse notifies the presence of pain and its severity to the physician, immediately. | ||
| Nurse documents the presence and severity of pain in the patient’s medical record. | ||
| Nurse implements pain-relief interventions. | ||
| Nurse documents the interventions in the patient’s medical record. | ||
| Nurse assesses the outcomes of the implemented interventions. | ||
| Total Score: | Mean Score: | Corrected Score (Mean %): |
Characteristics of participants. Shown in the table are the means, the standard deviation (±) or frequencsy n (percent)
| Variables | groups | ||
|---|---|---|---|
| LBT (33) | SNA (34) | ||
| Mean ± SD | Mean ± SD | ||
| Age (year) | 34 ± 6 | 34 ± 7 | |
| Work history (year) | 9 ± 5 | 9 ± 6 | |
| Gender | N (%) | N (%) | |
| Male | 9 (27) | 5 (15) | |
| Female | 24 (73) | 29 (85) | |
| Educational level | N (%) | N (%) | |
| BsC | 30 (91) | 34 (100) | |
| MsC | 3 (9) | 0 (0) | |
1. T-test, 2. Chi-square, 3. Exact fisher
LBT Lecture-Based Training,
SNA Social Networking App,
BsC Bachelor of Science,
MsC Master of Science
Nurse performance scores in the domain of pain diagnosis before and after the intervention. Shown in the table are the means, the standard deviation (±) and the confidence interval (95% CI)
| Performance scores | LBT group (33) | SNA group (34) | |
|---|---|---|---|
| Before | 33 ± 34 (21.5, 45) | 39 ± 28 (28.5, 49) | 0.46 |
| After | 82 ± 19 (75, 89) | 97 ± 8 (94, 100) | < 0.01 |
1. T-test
LBT Lecture-Based Training,
SNA Social Networking App
Nurse performance scores in the domain of pain management before and after the intervention. Shown in the table are the means, the standard deviation (±) and the confidence interval (95% CI)
| Performance scores | LBT group (33) | SNA group (34) | |
|---|---|---|---|
| Before | 20 ± 18.5 (13, 26) | 18 ± 11 (14, 22) | 0.59 |
| After | 29.5 ± 17 (23.5, 36) | 90 ± 118 (84, 96) | < 0.01 |
1. T-test
LBT Lecture-Based Training,
SNA Social Networking App