Relationships between professional attitudes/quality improvement actions, and response towards colleagues’ underperformance.

1<p>Multivariate linear mixed model with random intercept by hospital, adjusted for fixed effects at the country level (country), hospital level (number of beds, teaching status, and ownership) and patient level (gender and age).</p>2<p>Additionally adjusted for professional attitudes index.</p>3<p>Professional quality improvement actions modeled as a sum of the yes/no questions QA1–QA3 (range 0–3). Coefficient corresponds to a 1 unit increase (one additional “Yes” response to the question series).</p

Characteristics of professionalism survey respondents (grouping attending physicians and residents together)¹.

1<p>Excluding professionals who are missing responses for >2 out of 5 professional attitudes subscales.</p>2<p>Includes attending physicians and residents-in-training.</p

Summary of professional values as defined by the Physician’s Charter (1) and the Code of Ethics for Nurses (2).

<p>Summary of professional values as defined by the Physician’s Charter (1) and the Code of Ethics for Nurses (2).</p

Scale mean (SD) scores, and item median (IQR) scores for physicians and nurses separately.

1<p>Median (Q1–Q3) provided for individual likert scale items (range 1–5), mean (SD) provided for subscales (range 1–5) and binary type items (range 0 or 1).</p>2<p>For likert scale items, percent of respondents who “somewhat agree” or “strongly agree”, for binary type items, percent of respondents answering “yes”.</p>3<p>Professional attitudes score = sum (improving quality of care, maintaining professional competence, fulfilling professional responsibility, Interprofessional collaboration) – 4 (ranges from 0–16).</p>4<p>Interprofessional collaboration = mean of shared education and collaboration and physician authority.</p>5<p>All professional behaviour items are binary (Yes/No) type items.</p>6<p>Professional reactions to colleagues’ performance not aggregated as a subscale.</p>7<p>Sample size restricted to those (physicians/nurses) who observed the specific type of underperformance in the past 3 years.</p

Characteristics of hospitals participating in study.

<p>Characteristics of hospitals participating in study.</p

Item and scale characteristics, internal consistency, reliability and item-total correlations, by profession.

1<p>Sample size (for physicians/nurses), excludes respondents who are missing responses for >2 out of 5 professional attitudes subscales.</p>2<p>Sample size for physicians/nurses.</p

Relationship between professional attitudes and quality improvement actions.

1<p>Multivariate linear mixed model with random intercept by hospital, adjusted for fixed effects at the country level (country), hospital level (number of beds, teaching status, and ownership) and patient level (gender and age).</p

Inter-scale correlations for physicians and nurses separately.

<p>Inter-scale correlations for physicians and nurses separately.</p

From good to excellent: Improving clinical departments’ learning climate in residency training

<p><b>Introduction:</b> The improvement of clinical departments’ learning climate is central to achieving high-quality residency training and patient care. However, improving the learning climate can be challenging given its complexity as a multi-dimensional construct. Distinct representations of the dimensions might create different learning climate groups across departments and may require varying efforts to achieve improvement. Therefore, this study investigated: (1) whether distinct learning climate groups could be identified and (2) whether contextual factors could explain variation in departments’ learning climate performance.</p> <p><b>Methods:</b> This study included departments that used the Dutch Residency Educational Climate Test (D-RECT) through a web-based system in 2014–2015. Latent profile analysis was used to identify learning climate groups and multilevel modeling to predict clinical departments’ learning climate performance.</p> <p><b>Results:</b> The study included 1730 resident evaluations. Departments were classified into one of the four learning climate groups: substandard, adequate, good and excellent performers. The teaching status of the hospital, departments’ average teaching performance and percentage of time spent on educational activities by faculty-predicted departments’ learning climate performance.</p> <p><b>Discussion:</b> Clinical departments can be successfully classified into informative learning climate groups. Ideally, given informative climate grouping with potential for cross learning, the departments could embark on targeted performance improvement.</p

Unadjusted and adjusted associations of overall learning climate score with overall teaching performance of faculty as evaluated by residents.

a<p>Adjusted for evaluating resident’s gender and residency year, and evaluation period in cross-classified random-intercept linear mixed regression.</p