Robyn Synnott

The relationship between resilience and personality traits in doctors: implications for enhancing well being

Objective. The health and well being of medical doctors is vital to their longevity and safe practice. The concept of resilience is recognised as a key component of well being and is an important factor in medical training to help doctors learn to cope with challenge, stress, and adversity. This study examined the relationship of resilience to personality traits and resilience in doctors in order to identify the key traits that promote or impair resilience. Methods. A cross sectional cohort of 479 family practitioners in practice across Australia was studied. The Temperament and Character Inventory measured levels of the seven basic dimensions of personality and the Resilience Scale provided an overall measure of resilience. The associations between resilience and personality were examined by Pearson product-moment correlation coefficients, controlling for age and gender (α = 0.05 with an accompanying 95% confidence level) and multiple regression analyses. Results. Strong to medium positive correlations were found between Resilience and Self-directedness (r = .614, p < .01), Persistence (r = .498, p < .01), and Cooperativeness (r = .363, p < .01) and negative with Harm Avoidance (r = .−555, p < .01). Individual differences in personality explained 39% of the variance in resilience [F(7, 460) = 38.40, p < .001]. The three traits which contributed significantly to this variance were Self-directedness (β = .33, p < .001), Persistence (β = .22, p < .001) and Harm Avoidance (β = .19, p < .001). Conclusion. Resilience was associated with a personality trait pattern that is mature, responsible, optimistic, persevering, and cooperative. Findings support the inclusion of resilience as a component of optimal functioning and well being in doctors. Strategies for enhancing resilience should consider the key traits that drive or impair it.

Predictors of government subsidized pharmaceutical use in patients with diabetes or cardiovascular disease in a primary care setting: evidence from a prospective randomized trial.

Abstract Objectives: This study uses data from a prospective randomized controlled trial to estimate predictors of pharmaceutical expenditure in diabetes (DM) or cardiovascular disease (CVD) patients. Identifying drivers of pharmaceutical use and the extent to which they are modifiable may inform cost-effective policy-making. Methods: The trial followed 260 patients aged >18 years (mean 68) from three general practices for 12 months. Patients had type 2 diabetes (90 patients) or cardiovascular disease (170 patients). Costs for pharmaceuticals prescribed on the Pharmaceutical Benefits Scheme (PBS) were obtained retrospectively at 12 months. Sociodemographic data and health-related quality-of-life (QoL) were recorded from questionnaires. Clinical measures (including body mass index (BMI), blood pressure, high and low density lipoprotein (LDL), and HbA1c) were also collected. Results: Mean pharmaceutical costs for DM patients (AU

Revenue effects of practice nurse-led care for chronic diseases

4119) was greater than CVD patients (AU

A systematic review of the effect of educational interventions for urinary and faecal incontinence by health care staff/carers/clients in aged care, on level of knowledge, frequency of incontinence episodes and hours spent on the management of incontinence episodes.

2424). The largest contributor to costs in both groups was pharmaceuticals used for management of conditions other than CVD or DM. QoL (EQ5D) and BMI were significant predictors of costs in both groups. A history of cardiac events, HbA1c, age, and unemployment were significant predictors of costs in the DM group. A diagnosis of heart failure, frequency of hospital admissions, and LDL levels were significant predictors of costs in the CVD group. Roughly one third of total variation of costs can be explained by the regressors in both models. Limitations: Generalizability will be limited as data was derived from a trial and the study was not powered for this post-hoc analysis. Missing data imputation and self-reporting bias may also impact on results. Conclusions: Factors such as QoL BMI, HbA1c levels, and a history of cardiac events are significant predictors of costs. The results suggest there may be a place for interventions that improve quality-of-life and concurrently reduce pharmaceutical costs in patients with CVD or DM.

A decade of Australian Rural Clinical School graduates--where are they and why?

OBJECTIVE To determine the economic feasibility in Australian general practices of using a practice nurse (PN)-led care model of chronic disease management. METHODS A cost-analysis of item numbers from the Medicare Benefit Schedule (MBS) was performed in three Australian general practices, one urban, one regional and one rural. Patients (n =254; >18 years of age) with chronic conditions (type 2 diabetes, hypertension, ischaemic heart disease) but without unstable or major health problems were randomised into usual general practitioner (GP) or PN-led care for management of their condition over a period of 12 months. After the 12-month intervention, total MBS item charges were evaluated for patients managed for their stable chronic condition by usual GP or PN-led care. Zero-skewness log transformation was applied to cost data and log-linear regression analysis was undertaken. RESULTS There was an estimated A

Outcomes and opportunities: a nurse-led model of chronic disease management in Australian general practice

129 mean increase in total MBS item charges over a 1-year period (controlled for age, self-reported quality of life and geographic location of practice) associated with PN-led care. The frequency of GP and PN visits varied markedly according to the chronic disease. CONCLUSIONS Medicare reimbursements provided sufficient funding for general practices to employ PNs within limits of workloads before the new Practice Nurse Incentive Program was introduced in July 2012.

A nurse led model of chronic disease care - An interim report

Brent Hodgkinson, Kay Josephs , Eliza Leira , Robyn Synnott, Delsey Hegney 1 Senior Research Facilitator, Blue Care Business Improvement Team. Contact: (07) 3720 5306 Email: b.hodgkinson@bluecare.org.au 2 Clinical Researcher (Continence), UQ/ Blue Care Research and Practice Development Centre & Continence Advisor Blue Care. Contact: (07) 3720 5620 (W) Email: k.josephs@bluecare.org.au 3 Honours Student, School of Nursing, University of Queensland. Contact: Email: elizaleira@gmail.com