Reactivation will enable you to use the vocabulary trainer and any other programs. Send us feedback. Are you missing a word, phrase or translation? Submit a new entry. Compile a new entry. The first meeting was a bitter disappointment, since Francis was not only ugly but also completely uninterested in marriage and his wife. His preference was for religious literature and not for politics, and so after his father, Ferdinand II, died, it was not he but rather his mother, Maria Theresa von Habsburg, who ruled the country.
Politically uninterested he tried to realize his high ideals, which failed due to political and personal reasons. He finds his artistic identity in compromising: www. Both groups were badly informed about or rather uninterested in the origin of diseases and health behaviour. Judgeing from the results we obtained from the areas studied in this trial, the new shift system does not seem to have any negative health effect. The entry has been added to your favourites.
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Psychologie Des Gesundheitsverhaltens (German Edition) [Maria Stahl] on ethtafibtapa.cf *FREE* shipping on qualifying offers. Einsendeaufgabe aus dem Jahr. Editorial Reviews. Language Notes. Text: German Buy Psychologie des Gesundheitsverhaltens (German Edition): Read Kindle Store Reviews - Amazon. com.
Please try again. Thank you! Your message has now been forwarded to the PONS editorial department. Close Send feedback. How can I copy translations to the vocabulary trainer? Additional insights could be gained by examining strictly elective surgical procedures, such as total hip replacement or cholecystectomy. While we clearly show that discrimination in waiting times by insurance status does occur in the German acute hospital sector, we do not know whether discrimination in waiting times carries on to discrimination in the treatment quality.
This is an important issue, especially as there is very little transparency in quality of treatment in Germany. In the end, patients cannot judge which hospitals provide the best treatment. As long as quality remains nontransparent, it will be easier for hospitals to discriminate amongst patients by insurance status.
Currently 5. The lag was meant to be kept short in order to minimize the risk that a sudden change in a hospital's capacity utilization could significantly impact waiting times.
Where possible, in order to prevent the interviewer from being re-identified by the personnel of the hospital, second calls were done by different callers. Furthermore, caller identification for all outgoing calls was blocked. Since the market structure has an impact on treatment alternatives on site, this topic should be considered by further research either [ 19 , 20 ].
The authors thank Dr. Melanie Schubert, Dipl. Elisabeth Steinhagen, cand. Ivo Grons, Dipl. Sebastian Schmidt, Dipl. The authors also thank Dipl. Annika Herr and Dipl. Nadine Wiese for helpful comments. Finally we thank BA, Mphil. Shawna N. Smith for helpful comments and proofreading the whole manuscript. This article is published under license to BioMed Central Ltd. Research Open Access. The influence of insurance status on waiting times in German acute care hospitals: an empirical analysis of new data.
Problem Existing analyses of the determinants for waiting times in Germany are a based on patient self-reports and b do not cover the inpatient sector. Methods We requested individual appointments from hospitals within an experimental study design, allowing us to analyze the impact of PHI versus SHI on waiting times Asplin et al. Conclusion Discrimination in waiting times by insurance status does occur in the German acute hospital sector.
Therefore we formulate the following hypothesis: Hypothesis: In German acute care hospitals, private patients will get an appointment for treatment faster than compulsorily insured patients with the same medical indication. Study Design Data on waiting times for appointments at acute care hospitals in Germany was not available prior to this study. The sample size is listed in table 1. The number of hospitals actively investigating the insurance status is displayed in Table 1 , column 4.
Out of a total of hospitals, actively investigated the insurance status. To form a control group, these hospitals were called again in the second round. Thus a total of appointments were made. Table 2 displays the variables generated. Table 2 List of Variables. Table 3 presents descriptive statistics for the data. It displays the number of observations, the mean, the standard deviation and the minimum and maximum values of the data.
All analyses were performed using Stata 9. In the upper portion of the table, statistics for the full sample are shown. The lower portion of the table presents the statistics for the subsample of hospitals actively requesting the insurance type and therefore called twice. The upper part of table 3 shows that hospitals that investigated insurance type have on-average higher waiting times than those who did not.
Population-based validation of a German version of the Brief Resilience Scale. On their route from one provincial stage to the other they experience the change from a state theatre group to a private enterprise.. Recruitment took place by call-ups for project participation in 9 different nation-wide magazines and by flyers at appropriate lectures, congresses and self-help organisations. Schachinger, G. Der Naturarzt 4 , , Your rating has been recorded. You may send this item to up to five recipients.
Further, the data reveal that average waiting times differ by clinical condition and hospital ownership. As the lower portion of the table shows, average waiting times clearly differ by insurance type, with PHI holders having shorter waiting times than SHI holders. The following statistical analyses will deliver more detailed insights regarding the impact of insurance status on waiting times.
Table 3 Descriptive Statistics. The logarithmic gross waiting period 5 is used as the dependent variable since gross waiting time is skewed left, indicated by maximum values strongly varying from the mean. This allows for an endogenous variable with approximate Gaussian distribution which is necessary for an undistorted estimator in the context of an OLS regression.
The short form of the estimated equation is:. Thus, insurance status is a significant predictor of waiting times.
go On average PHI-holders' wait was 1. This hints at overall lower capacity utilization in these hospitals as compared to those which actively investigated the insurance type of the patients. Table 4 Determinants of Waiting Time and Questioning. This result does not change significantly if the time period to the appointment is extended. Both of these differences - for the period of one week as well as for two weeks - are statistically significant, as shown by the results in Table 5.
Table 5 Proportion of patients with an appointment for surgery after 1 or 2 weeks respectively. Table 6 documents the results of the detailed analysis for each of the three clinical conditions. Because of the considerably lower number of observations, a number of estimation coefficients are no longer significant. However, it can be said that insurance status within each observed diagnosis groups shows the expected effect.
In addition, the other estimation coefficients confirm our fundamental analysis. Only the estimator of privately funded hospitals for stenosis is not in accordance with the core analysis. Furthermore, a significant negative correlation with waiting time can be shown for the Weber B Fracture and stenosis diagnoses for the privately owned hospitals as compared to the control group, hospitals owned by charitable organizations, in spite of the relatively small sample size. Table 6 Diagnoses. R 2 or Pseudo R 2 Mc Fadden 0. Acknowledgements The authors thank Dr. Medical Care.
New England Journal of Medicine. International Journal for Equity in Health. The American Economic Review. Journal of Health Economics. Journal of Public Economics.
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Write a review Rate this item: 1 2 3 4 5. Preview this item Preview this item. Subjects Fragebogen.