| Literature DB >> 18840296 |
Isabelle Fuss1, Matthias Nübling, Hans-Martin Hasselhorn, David Schwappach, Monika A Rieger.
Abstract
BACKGROUND: Germany currently experiences a situation of major physician attrition. The incompatibility between work and family has been discussed as one of the major reasons for the increasing departure of German physicians for non-clinical occupations or abroad. This study investigates predictors for one particular direction of Work-Family Conflict--namely work interfering with family conflict (WIF)--which are located within the psychosocial work environment or work organisation of hospital physicians. Furthermore, effects of WIF on the individual physicians' physical and mental health were examined. Analyses were performed with an emphasis on gender differences. Comparisons with the general German population were made.Entities:
Mesh:
Year: 2008 PMID: 18840296 PMCID: PMC2577658 DOI: 10.1186/1471-2458-8-353
Source DB: PubMed Journal: BMC Public Health ISSN: 1471-2458 Impact factor: 3.295
Hospital-specific single items capturing psychosocial working conditions of physicians (response scales)
| During the last 12 months, did you take part in a vocational training? |
| Who paid your last vocational training? |
| Was it made possible for you to change your duty for vocational training in the past? |
| Does your superior acknowledge the efforts and results of your work? |
| Do your colleagues acknowledge the efforts and results of your work? |
| How often do you experience competitive pressure among your colleagues? |
| If you worked overtime, would you report it to your superior? |
| How often was the duty roster changed on short-notice (i.e., within 1 to 3 days) during the last 3 months? |
| If the duty roster was changed on short-notice, how often did you have to work on a day off? |
| If the duty roster was changed on short-notice, how often did you have to work on a weekend off? |
| If the duty roster was changed on short-notice, how often were your family/personal preferences ignored? |
| If the duty roster was changed on short-notice, how often did you have to continue working after your duty? |
| If the duty roster was changed on short-notice, how often did you have to postpone a planned vacation? |
| In the case of a changed duty roster on short-notice, how stressful was it for you to work on a day off? |
| In the case of a changed duty roster on short-notice, how stressful was it for you to work on a weekend off? |
| In the case of a changed duty roster on short-notice, how stressful was it for you when family/personal preferences were ignored? |
| In the case of a changed duty roster on short-notice, how stressful was it for you to continue working after your duty? |
| In the case of a changed duty roster on short-notice, how stressful was it for you to postpone a planned vacation? |
| What do you think are the most likely reasons for short-notice changes of the duty roster? |
| How often were team meetings (with colleagues and superiors) used as a platform to discuss criticism and improvement strategies? |
| During the last 12 months, how often did you go to work despite own illness? |
Sociodemographic characteristics of the sample of German hospital physicians, n = 296
| absolute ( | relative (%) | |
| A | 110 | 37.2 |
| B | 186 | 62.8 |
| up to 34 years | 126 | 42.6 |
| 35 – 44 years | 98 | 33.1 |
| 45 – 54 years | 44 | 14.9 |
| 55 years and older | 21 | 7.1 |
| male | 178 | 60.1 |
| female | 114 | 38.5 |
| resident | 128 | 43.2 |
| board certified specialist | 72 | 24.3 |
| attending | 76 | 25.7 |
| head of department | 16 | 5.4 |
| surgery | 57 | 19.3 |
| internal medicine | 53 | 17.9 |
| anesthesiology | 48 | 16.2 |
| pediatrics | 46 | 15.5 |
| small surgical specialities | 31 | 10.5 |
| patient-distant specialities | 36 | 12.2 |
| neurological-dermatological | 20 | 6.8 |
| others/no answer | 5 | 1.7 |
| Germany | 277 | 93.6 |
| foreign country | 18 | 6.1 |
| living with a partner | 221 | 74.7 |
| living with a child under 15 yrs | 114 | 38.5 |
| part-time | 35 | 11.8 |
| full-time | 261 | 88.2 |
| temporary | 139 | 47.0 |
| permanent | 155 | 52.4 |
Correlation between characteristics of work organisation and WIF: bivariate analysis (hierarchy according to eta; +/- indicating positive/negative correlation)
| eta | eta2 | ||||
| number of days gone to work despite own illness | + | 16.296 | < .001 | .386 | .146 |
| planned vacations postponed (frequency) | + | 13.074 | < .001 | .291 | .085 |
| staff shortage as presumed reasons for changes in duty roster | + | 23.098 | < .001 | .270 | .073 |
| strain due to a lost weekend off | + | 10.413 | < .001 | .264 | .070 |
| strain due to changes in duty roster ignoring personal requests | + | 9.335 | < .001 | .252 | .063 |
| colleagues on vacation as presumed reasons for changes in duty roster | + | 14.230 | < .001 | .215 | .046 |
| exemption from duty during training | + | 7.688 | .001 | .225 | .051 |
| personal requests ignored at changes in duty roster (frequency) | + | 7.374 | .001 | .221 | .049 |
| strain due to being called on duty out of a day off | + | 7.063 | .001 | .221 | .049 |
| strain due to having to continue to work after shift | + | 6.144 | .002 | .205 | .042 |
| competitive pressure in department | + | 4.072 | .003 | .232 | .054 |
| colleagues on maternity/parental leave as presumed reasons for changes in duty roster | + | 8.776 | .003 | .171 | .029 |
| general frequency changes in duty roster on short notice | + | 4.754 | .003 | .218 | .047 |
| appreciation of work by superiors | - | 3.502 | .008 | .216 | .047 |
| continue working after shift (frequency) | + | 3.922 | .021 | .164 | .027 |
Predictors for WIF: multivariate analysis of sociodemographic variables, psychosocial work environment (COPSOQ), and hospital-specific items of work organisation
| cumul. | stand. beta | significance | |
| quantitative demands | .32 | .46 | |
| number of days gone to work despite own illness | .39 | .22 | |
| age group | .42 | -.19 | |
| frequency to postpone vacation due to changes on duty roster | .44 | .16 | |
| sense of community | .45 | -.10 |
Significant gender differences found in predictors and outcome variables for WIF
| mean | sd | eta | |||||
| Work-Family Conflict (WIF) | female | 73.73 | 114 | 23.099 | .021 | .886 | .01 |
| male | 74.15 | 177 | 25.487 | ||||
| total | 73.99 | 291 | 24.539 | ||||
| sense of community | female | 77.67 | 114 | 15.942 | 8.976 | .003 | .17 |
| male | 72.05 | 178 | 15.428 | ||||
| total | 74.24 | 292 | 15.843 | ||||
| job satisfaction | female | 52.38 | 114 | 15.914 | 4.734 | .030 | .13 |
| male | 56.92 | 178 | 18.286 | ||||
| total | 55.15 | 292 | 17.511 | ||||
| Work Ability Index (WAI) | female | 77.99 | 106 | 13.388 | 5.139 | .024 | .13 |
| male | 81.36 | 176 | 11.247 | ||||
| total | 80.09 | 282 | 12.183 | ||||
| Copenhagen Burnout | female | 51.05 | 114 | 17.712 | 9.731 | .002 | .18 |
| Inventory (CBI) | male | 44.25 | 178 | 18.477 | |||
| total | 46.90 | 292 | 18.453 | ||||
Predictors derived from multivariate analysis, outcome variables derived from bi-variate analysis.
Correlation of WIF and outcome variables (scales or indices)
| Pearson's | |||
| behavioural stress symptoms | .58 | < .01 | 294 |
| Copenhagen Burnout Inventory (CBI), personal burnout | .55 | < .01 | 295 |
| satisfaction with life scale (SWLS) (5–35) | -.42 | < .01 | 295 |
| job satisfaction | -.36 | < .01 | 295 |
| intention to leave | .37 | < .01 | 294 |
| cognitive stress symptoms | .31 | < .01 | 295 |
| Work Ability Index (0–100) | -.30 | < .01 | 285 |
| general health status | -.26 | < .01 | 294 |
Figure 1Mean values of outcome variables by WIF level (grouped into quartils).