| Literature DB >> 35627858 |
Teresa Evaristo-Chiyong1, Manuel Antonio Mattos-Vela1, Andrés A Agudelo-Suárez2, Ana Del Carmen Armas-Vega3, Juan Carlos Cuevas-González4, Clarisse Virginia Díaz-Reissner5, Ana Cristina López Torres6, Cecilia María Martínez-Delgado7, Manuel Amed Paz-Betanco8, María Antonieta Pérez-Flores9, Sylvia Piovesan-Suárez10, Adriana Pistochini11, Yajaira Romero-Uzcátegui12.
Abstract
This study aimed to determine the general labor well-being of Latin American dentists according to sociodemographic characteristics during the COVID-19 pandemic. A cross-sectional study was conducted in a final sample of 2214 participants from 11 countries. A validated online questionnaire on general work well-being was used (data collection period from 1 June to 10 July 2021), containing two dimensions: psychosocial well-being and collateral effects. The sociodemographic characteristics of the dentists and their perception of the economic impact of the pandemic were also recorded. A multivariate linear regression analysis was performed (hierarchical regression model) to evaluate the joint effect of the explanatory variables on labor well-being and the changes in the variance between each model. A score of psychosocial well-being of 233.6 + 40.2 and collateral effects of 45 + 20.1 was found. Psychosocial well-being was associated with sex, country of origin, academic training achieved, type of dental activity, and perceived impact during the pandemic (p < 0.05). Somatization was frequently manifested through back pain (88.2%) and muscular tensions (87.2%). Women, those who worked 41 or more hours and had between 1 to 15 years of professional experience presented a greater collateral effect (p < 0.001). The impact of the COVID-19 pandemic a year and a half after it began on the labor well-being of Latin American dentists was evidenced with important interactions with social characteristics.Entities:
Keywords: COVID-19; dentists; health surveys; post-traumatic stress disorder; psychological; stress; working conditions
Mesh:
Year: 2022 PMID: 35627858 PMCID: PMC9141737 DOI: 10.3390/ijerph19106317
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Characteristics of the participating dentists (n = 2214).
| Variables | N | % |
|---|---|---|
|
| ||
| Males | 733 | 33.1 |
| Females | 1481 | 66.9 |
|
| ||
| 20–34 | 796 | 36.0 |
| 35–49 | 856 | 38.7 |
| ≥51 | 562 | 25.4 |
|
| ||
| Peru | 293 | 13.2 |
| Chile | 163 | 7.4 |
| Mexico | 186 | 8.4 |
| Colombia | 323 | 14.6 |
| Argentina | 206 | 9.3 |
| Ecuador | 314 | 14.2 |
| Uruguay | 69 | 3.1 |
| Venezuela | 237 | 10.7 |
| Paraguay | 205 | 9.3 |
| Nicaragua | 168 | 7.6 |
| Costa Rica | 50 | 2.3 |
|
| ||
| ≤15 | 1204 | 54.4 |
| 16–30 | 745 | 33.6 |
| ≥31 | 265 | 12.0 |
|
| ||
| General dentist | 849 | 38.3 |
| Clinical specialist | 794 | 35.9 |
| MSc | 417 | 18.8 |
| PhD | 154 | 7.0 |
|
| ||
| Endodontics and cariology | 124 | 9.5 |
| Maxillofacial Surgery | 154 | 11.7 |
| Prosthodontics | 46 | 3.5 |
| Periodontics and implantology | 102 | 7.8 |
| Pediatric dentistry | 177 | 13.5 |
| Orthodontics | 209 | 15.9 |
| Forensic dentistry | 5 | 0.4 |
| Radiology | 9 | 0.7 |
| Public health | 77 | 5.9 |
| Other | 217 | 16.5 |
| More than one | 192 | 14.6 |
|
| ||
| Teaching/Research | 139 | 6.3 |
| Clinical assistance | 1431 | 64.6 |
| Both | 644 | 29.1 |
|
| ||
| 1–20 | 578 | 26.1 |
| 21–40 | 980 | 44.3 |
| ≥41 | 656 | 29.6 |
|
| ||
| Fixed | 723 | 32.7 |
| Variable | 1491 | 67.3 |
|
| ||
| None | 250 | 11.3 |
| Mild | 444 | 20.1 |
| Moderate | 997 | 45.0 |
| Severe | 523 | 23.6 |
Psychosocial well-being and total score for collateral effects according to characteristics of the participants.
| Variables | Psychosocial Well-Being (42–294) | Total Score for Collateral Effects (13–91) | ||||||
|---|---|---|---|---|---|---|---|---|
| Mean | SD | Median | Mean | SD | Median | |||
|
| ||||||||
| Males | 241.23 | 36.3 | 246 | <0.001 | 40.43 | 18.62 | 37 | <0.001 |
| Females | 229.87 | 41.5 | 236 | 47.3 | 20.36 | 47 | ||
|
| ||||||||
| 20–34 | 233.18 | 40.8 | 239 | 0.578 | 46.50 a | 19.97 | 46 | <0.001 |
| 35–49 | 233.1 | 39.9 | 240 | 46.08 a | 20.29 | 44 | ||
| ≥51 | 235.1 | 39.9 | 240 | 41.32 b | 19.41 | 38 | ||
|
| ||||||||
| South America | 231.23 | 40.6 | 236.5 | <0.001 | 45.48 | 20.08 | 44 | 0.022 |
| Mexico and Central America | 244.41 | 36.7 | 249 | 42.97 | 19.86 | 40 | ||
|
| ||||||||
| ≤15 | 232.22 a | 41.2 | 238 | 0.039 | 46.92 a | 20.06 | 46 | <0.001 |
| 16–30 | 234.14 ab | 38.2 | 240 | 44.34 b | 19.82 | 42 | ||
| ≥31 | 238.66 b | 40.9 | 243 | 38.36 c | 19.23 | 34 | ||
|
| ||||||||
| General dentist | 227.30 a | 40.9 | 232 | <0.001 | 46.04 | 19.93 | 45 | 0.143 |
| Clinical specialist | 236.09 b | 39.9 | 242 | 44.92 | 20.28 | 43 | ||
| MSc | 241.18 b | 36.7 | 245 | 43.95 | 19.73 | 40 | ||
| PhD | 235.47 b | 42.6 | 248 | 42.89 | 20.37 | 40 | ||
|
| ||||||||
| Endodontics and cariology, Maxillofacial Surgery, Prosthodontics, Periodontics and implantology | 237.79 | 39 | 244.5 | 0.581 | 44.07 | 20.43 | 40 | 0.466 |
| Pediatric dentistry, Orthodontics, Forensic dentistry, Radiology, Public health, Other | 237.84 | 38.8 | 244 | 44.11 | 19.89 | 41 | ||
| More than one | 234.22 | 41.7 | 240.5 | 46.05 | 20.46 | 47 | ||
|
| ||||||||
| Teaching/Research | 233.85 ab | 42.7 | 242 | <0.001 | 47.57 ab | 19.46 | 47 | 0.015 |
| Clinical assistance | 230.33 a | 40.7 | 235 | 45.54 a | 19.99 | 44 | ||
| Both | 240.93 b | 37.5 | 247 | 43.34 b | 20.25 | 40 | ||
|
| ||||||||
| 1 to 20 | 230.75 | 41.3 | 236 | 0.143 | 41.85 a | 19.6 | 39 | <0.001 |
| 21 to 40 | 234.84 | 40.3 | 241 | 44.38 b | 19.84 | 42 | ||
| ≥31 | 234.38 | 38.9 | 241 | 48.80 c | 20.22 | 48 | ||
|
| ||||||||
| Fixed | 231.58 | 40.5 | 238 | 0.095 | 46.09 | 20.32 | 45 | 0.092 |
| Variable | 234.63 | 40.1 | 240 | 44.51 | 19.92 | 43 | ||
|
| ||||||||
| None | 245.16 a | 39.5 | 254.5 | <0.001 | 42.34 a | 20.77 | 40 | <0.001 |
| Mild | 243.22 a | 36.5 | 248 | 42.67 a | 18.97 | 40 | ||
| Moderate | 235.98 b | 36.8 | 240 | 44.37 a | 19.82 | 42 | ||
| Severe | 215.53 b | 43.8 | 219 | 49.57 b | 20.39 | 51 | ||
Different letters indicate statistically significant differences. The values in parentheses for the psychosocial well-being and the collateral effects indicate the minimum and maximum score to be reached by the dimension.
Specific collateral effects in the participants.
| Collateral Effects | Mean | SD | Median | No Presence (1) | Presence (2–7) |
|---|---|---|---|---|---|
|
| |||||
| Digestive disorders | 2.96 | 1.973 | 2 | 790 (35.7%) | 1424 (64.3%) |
| Headache | 3.26 | 1.972 | 3 | 60 (27.3%) | 1610 (72.7%) |
| Insomnia | 3.01 | 1.986 | 2 | 753 (34.0%) | 1461 (66.0%) |
| Backache | 4.37 | 2.011 | 5 | 261 (11.8%) | 1953 (88.2%) |
| Muscle tensions | 4.29 | 2.014 | 5 | 284 (12.8%) | 1930 (87.2%) |
|
| |||||
| Work overload | 3.86 | 2.051 | 4 | 392 (17.7%) | 1822 (82.3%) |
| Emotional stress | 3.78 | 2.085 | 4 | 428 (19.3%) | 1786 (80.7%) |
| Physical exhaustion | 4.11 | 1.995 | 4 | 276 (12.5%) | 1938 (87.5%) |
| Mental saturation | 3.81 | 2.071 | 4 | 409 (18.5%) | 1805 (81.5%) |
|
| |||||
| Bad mood | 3.04 | 1.88 | 3 | 632 (28.5%) | 1582 (71.5%) |
| Low professional achievement | 2.97 | 1.902 | 2 | 738 (33.3%) | 1476 (66.7%) |
| Depersonalized treatment | 2.67 | 1.857 | 2 | 913 (41.2%) | 1301 (58.8%) |
| Frustration | 2.91 | 1.91 | 2 | 755 (34.1%) | 1459 (65.9%) |
The presence of different collateral effects was defined with the responses from 2 to 7 according to the questionnaire.
Total scores for collateral effects according to the origin country of participants.
| Origin Country | Psychosocial Well-Being (42–294) * | Collateral Effects (13–91) * | ||||
|---|---|---|---|---|---|---|
| Mean | SD | Median | Mean | SD | Median | |
| Peru | 235.97 | 37.64 | 240 | 41.45 | 18.55 | 39 |
| Chile | 233.92 | 36.86 | 239 | 49.67 | 20.87 | 52 |
| Mexico | 245.02 | 36.33 | 249 | 44.87 | 20.67 | 40 |
| Colombia | 231.11 | 41.36 | 237 | 43.72 | 19.57 | 43 |
| Argentina | 224.08 | 43.39 | 232 | 52.4 | 19.43 | 55 |
| Ecuador | 241.03 | 39.26 | 247 | 46.55 | 20.44 | 44.5 |
| Uruguay | 230.26 | 35.01 | 235 | 47.33 | 18.8 | 48 |
| Venezuela | 225.66 | 42.38 | 230 | 39.41 | 18.72 | 36 |
| Paraguay | 221.46 | 40.78 | 222 | 48.52 | 21.08 | 47 |
| Nicaragua | 244.73 | 35.5 | 248 | 40.01 | 18.44 | 37 |
| Costa Rica | 241.06 | 42.3 | 252 | 45.86 | 20.51 | 40 |
* Kruskal–Wallis test: p-value < 0.001 for both dimensions (statistically significant differences when all countries in the sample are considered).
Multiple linear regression model of the psychosocial well-being of dentists (n = 2214).
| Variables | Determination Coefficient (R2) | Change in R2 | Constant | Nonstandardized Regression Coefficient | Standardized Regression Coefficient | 95% Confidence Interval | ||||
|---|---|---|---|---|---|---|---|---|---|---|
|
| <0.001 | |||||||||
| Sex (F) | 0.03 | 0.03 | <0.001 | 0.527 | −0.034 | −0.105 | −0.047 | −0.02 | <0.001 | |
| Age | 0.008 | 0.042 | 0 | 0.016 | 0.056 | |||||
| Country of origin (México—Central America) | 0.052 | 0.132 | 0.035 | 0.069 | <0.001 | |||||
|
| ||||||||||
| Sex (F) | 0.044 | 0.014 | <0.001 | 0.515 | −0.03 | −0.094 | −0.044 | −0.017 | <0.001 | <0.001 |
| Age | −0.013 | −0.064 | −0.026 | 0.001 | 0.072 | |||||
| Country of origin (México—Central America) | 0.052 | 0.132 | 0.035 | 0.068 | <0.001 | |||||
| Clinical experience (years) | 0.018 | 0.083 | 0.003 | 0.033 | 0.016 | |||||
| Academic education | 0.019 | 0.113 | 0.011 | 0.026 | <0.001 | |||||
|
| ||||||||||
| Sex (F) | 0.116 | 0.072 | <0.001 | 0.551 | −0.027 | −0.085 | −0.04 | −0.015 | <0.001 | <0.001 |
| Age | −0.005 | −0.025 | −0.018 | 0.008 | 0.465 | |||||
| Country of origin (México—Central America) | 0.048 | 0.122 | 0.032 | 0.064 | <0.001 | |||||
| Clinical experience (years) | 0.016 | 0.072 | 0.001 | 0.03 | 0.031 | |||||
| Academic education | 0.014 | 0.086 | 0.007 | 0.021 | <0.001 | |||||
| Dental activity | 0.016 | 0.058 | 0.005 | 0.027 | 0.006 | |||||
| Weekly working hours | −0.007 | −0.034 | −0.015 | 0.001 | 0.104 | |||||
| Type of salary (variable) | 0.048 | 0.147 | 0.034 | 0.062 | <0.001 | |||||
| Economic impact of the pandemic | −0.046 | −0.28 | −0.053 | −0.039 | <0.001 | |||||
Multiple linear regression model of collateral effects in dentists. (n = 2214).
| Variables | Determination Coefficient (R2) | Change in R2 | Constant | Nonstandardized Regression Coefficient | Standardized Regression Coefficient | 95% Confidence Interval | ||||
|---|---|---|---|---|---|---|---|---|---|---|
|
| <0.001 | |||||||||
| Sex (F) | 0.036 | 0.036 | <0.001 | 0.513 | 0.048 | 0.147 | 0.035 | 0.061 | <0.001 | |
| Age | −0.021 | −0.104 | −0.029 | −0.012 | <0.001 | |||||
| Country of origin (México—Central America) | −0.025 | −0.062 | −0.042 | −0.008 | 0.004 | |||||
|
| ||||||||||
| Sex (F) | 0.042 | 0.006 | <0.001 | 0.512 | 0.046 | 0.141 | 0.033 | 0.06 | <0.001 | <0.001 |
| Age | 0 | −0.001 | −0.014 | 0.014 | 0.975 | |||||
| Country of origin (México—Central America) | −0.025 | −0.063 | −0.042 | −0.008 | 0.003 | |||||
| Clinical experience (years) | −0.029 | −0.131 | −0.044 | −0.014 | <0.001 | |||||
| Academic education | 0 | 0.001 | −0.007 | 0.007 | 0.977 | |||||
|
| ||||||||||
| Sex (F) | 0.084 | 0.042 | <0.001 | 0.464 | 0.049 | 0.15 | 0.036 | 0.062 | <0.001 | <0.001 |
| Age | −0.001 | −0.003 | −0.014 | 0.013 | 0.927 | |||||
| Country of origin (México—Central America) | −0.02 | −0.05 | −0.037 | −0.003 | 0.018 | |||||
| Clinical experience (years) | −0.026 | −0.119 | −0.041 | −0.012 | <0.001 | |||||
| Academic education | 0 | 0.002 | −0.007 | 0.008 | 0.934 | |||||
| Dental activity | −0.014 | −0.051 | −0.026 | −0.003 | 0.017 | |||||
| Weekly working hours | 0.031 | 0.153 | 0.023 | 0.04 | <0.001 | |||||
| Type of salary (variable) | −0.028 | −0.085 | −0.042 | −0.013 | <0.001 | |||||
| Economic impact of the pandemic | 0.027 | 0.16 | 0.019 | 0.034 | <0.001 | |||||