Effects of age, gender and educational background on strength of motivation for medical school

The aim of this study was to determine the effects of selection, educational background, age and gender on strength of motivation to attend and pursue medical school. Graduate entry (GE) medical students (having Bachelor’s degree in Life Sciences or related field) and Non-Graduate Entry (NGE) medical students (having only completed high school), were asked to fill out the Strength of Motivation for Medical School (SMMS) questionnaire at the start of medical school. The questionnaire measures the willingness of the medical students to pursue medical education even in the face of difficulty and sacrifice. GE students (59.64 ± 7.30) had higher strength of motivation as compared to NGE students (55.26 ± 8.33), so did females (57.05 ± 8.28) as compared to males (54.30 ± 8.08). 7.9% of the variance in the SMMS scores could be explained with the help of a linear regression model with age, gender and educational background/selection as predictor variables. Age was the single largest predictor. Maturity, taking developmental differences between sexes into account, was used as a predictor to correct for differences in the maturation of males and females. Still, the gender differences prevailed, though they were reduced. Pre-entrance educational background and selection also predicted the strength of motivation, but the effect of the two was confounded. Strength of motivation appears to be a dynamic entity, changing primarily with age and maturity and to a small extent with gender and experience

Additive technology of obtaining products from ceramics

Created an original design of the device, which lets you create samples of thermoplastic ceramic slurry, which after sintering, are obtained ceramics with high strength and hardness parameters

Introducing Summative Progress Testing in Radiology Residency: Little Change in Residents’ Test Results After Transitioning from Formative Progress Testing

Introduction: Educational effects of transitioning from formative to summative progress testing are unclear. Our purpose was to investigate wheth

Are health care professionals able to judge cancer patients' health care preferences correctly? A cross-sectional study

Background: Health care for cancer patients is primarily shaped by health care professionals. This raises the question to what extent health care professionals are aware of patients' preferences, needs and values. The aim of this study was to explore to what extent there is concordance between patients' preferences in cancer care and patients' preferences as estimated by health care professionals. We also examined whether there were gender differences between health care professionals with regard to the degree in which they can estimate patients' preferences correctly. Methods: To obtain unbiased insight into the specific preferences of cancer patients, we developed the 'Cancer patients' health care preferences' questionnaire'. With this questionnaire we assessed a large sample of cancer patients (n = 386). Next, we asked health care professionals (medical oncologists, nurses and policymakers, n = 60) to fill out this questionnaire and to indicate preferences they thought cancer patients would have. Mean scores between groups were compared using Mann-Whitney tests. Effect sizes (ESs) were calculated for statistically significant differences. Results: We found significant differences (ESs 0.31 to 0.90) between patients and professionals for eight out of twenty-one scales and two out of eight single items. Patients valued care aspects related to expertise and attitude of health care providers and accessibility of services as more important than the professionals thought they would do. Health care professionals overestimated the value that patients set on particularly organisational and environmental aspects. We found significant gender-related differences between the professionals (ESs 0.69 to 1.39) for eight out of twenty-one scales and two out of eight single items. When there were significant differences between male and female healthcare professionals in their estimation of patients health care preferences, female health care professionals invariably had higher scores. Generally, female health care professionals did not estimate patients' preferences and needs better than their male colleagues. Conclusions: Health care professionals are reasonably well able to make a correct estimation of patients preferences, but they should be aware of their own bias and use additional resources to gain a better understanding of patients' specific preferences for each patient is different and ultimately the care needs and preferences will also be unique to the person

Pain catastrophizing predicts dropout of patients from an interdisciplinary chronic pain management programme: a prospective cohort study

Objective: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common- Sense Model of Self-Regulation (E-CSM). Methods: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. Results: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard deviation 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. Conclusion: Patients with chronic pain who catastrophize were more prone to dropout from this chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout

Pain catastrophizing predicts dropout of patients from an interdisciplinary chronic pain management programme : A prospective cohort study

OBJECTIVE: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common-Sense Model of Self-Regulation (E-CSM). METHODS: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. RESULTS: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard devition 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. CONCLUSION: Patients with chronic pain who catastrophize were more prone to dropout from this -chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout

Pain catastrophizing predicts dropout of patients from an interdisciplinary chronic pain management programme : A prospective cohort study

OBJECTIVE: To explore predictors of dropout of patients with chronic musculoskeletal pain from an interdisciplinary chronic pain management programme, and to develop and validate a multivariable prediction model, based on the Extended Common-Sense Model of Self-Regulation (E-CSM). METHODS: In this prospective cohort study consecutive patients with chronic pain were recruited and followed up (July 2013 to May 2015). Possible associations between predictors and dropout were explored by univariate logistic regression analyses. Subsequently, multiple logistic regression analyses were executed to determine the model that best predicted dropout. RESULTS: Of 188 patients who initiated treatment, 35 (19%) were classified as dropouts. The mean age of the dropout group was 47.9 years (standard devition 9.9). Based on the univariate logistic regression analyses 7 predictors of the 18 potential predictors for dropout were eligible for entry into the multiple logistic regression analyses. Finally, only pain catastrophizing was identified as a significant predictor. CONCLUSION: Patients with chronic pain who catastrophize were more prone to dropout from this -chronic pain management programme. However, due to the exploratory nature of this study no firm conclusions can be drawn about the predictive value of the E-CSM of Self-Regulation for dropout