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Identification of promising strategies to sustain improvements in hospital practice

Background: A quality improvement collaborative is an intensive project involving a combination of implementation strategies applied in a limited “breakthrough” time window. After an implementation project, it is generally difficult to sustain its success. In the current study, sustainability was described as maintaining an implemented innovation and its benefits over a longer period of time after the implementation project has ended. The aim of the study was to explore potentially promising strategies for sustaining the Enhanced Recovery After Surgery (ERAS) programme in colonic surgery as perceived by professionals, three to six years after the hospital had successfully finished a quality improvement collaborative. Methods: A qualitative case study was performed to identify promising strategies to sustain key outcome variables related to the ERAS programme in terms of adherence, time needed for functional recovery and hospital length of stay (LOS), as achieved immediately after implementation. Ten hospitals were selected which had successfully implemented the ERAS programme in colonic surgery (2006–2009), with success defined as a median LOS of 6 days or less and protocol adherence rates above 70%. Fourteen semi-structured interviews were held with eighteen key participants of the care process three to six years after implementation, starting with the project leader in every hospital. The interviews started by confronting them with the level of sustained implementation results. A direct content analysis with an inductive coding approach was used to identify promising strategies. The mean duration of the interviews was 37 minutes (min 26 minutes – max 51 minutes). Results: The current study revealed strategies targeting professionals and the organisation. They comprised internal audit and feedback on outcomes, small-scale educational booster meetings, reminders, changing the physical structure of the organisation, changing the care process, making work agreements and delegating responsibility, and involving a coordinator. A multifaceted self-driven promising strategy was applied in most hospitals, and in most hospitals promising strategies were suggested to sustain the ERAS programme. Conclusions: Joining a quality improvement collaborative may not be enough to achieve long-term normalisation of transformed care, and additional investments may be needed. The findings suggest that certain post-implementation strategies are valuable in sustaining implementation successes achieved after joining a quality improvement collaborative.

Effective Attentional Focus Strategies after Anterior Cruciate Ligament Reconstruction

Individuals after anterior cruciate ligament reconstruction (ACLR) have a high rate of reinjury upon return to competitive sports. Deficits in motor control may influence reinjury risk and can be addressed during rehabilitation with motor learning strategies. When instructing patients in performing motor tasks after ACLR, an external focus of attention directed to the intended movement effect has been shown to be more effective in reducing reinjury risk than an internal focus of attention on body movements. While this concept is mostly agreed upon, recent literature has made it clear that the interpretation and implementation of an external focus of attention within ACLR rehabilitation needs to be better described. The purpose of this commentary is to provide a clinical framework for the application of attentional focus strategies and guide clinicians towards effectively utilizing an external focus of attention in rehabilitation after ACLR.

The interpretation of forensic conclusions by professionals and students

Are professionals better at assessing the evidential strength of different types of forensic conclusions compared to students? In an online questionnaire 96 crime investigation and law students, and 269 crime investigation and legal professionals assessed three fingerprint examination reports. All reports were similar, except for the conclusion part which was stated in a categorical (CAT), verbal likelihood ratio (VLR) or numerical likelihood ratio (NLR) conclusion with high or low evidential strength. The results showed no significant difference between the groups of students and professionals in their assessment of the conclusions. They all overestimated the strength of the strong CAT conclusion compared to the other conclusion types and underestimated the strength of the weak CAT conclusion. Their background (legal vs. crime investigation) did have a significant effect on their understanding. Whereas the legal professionals performed better compared to the crime investigators, the legal students performed worse compared to crime investigation students.

Ethnographic strategies for making the familiar strange: Struggling with ‘distance’ and ‘immersion’ among Moroccan-Dutch students

Ethnographic fieldwork is a balancing act between distancing and immersing. Fieldworkers need to come close to meaningfully grasp the sense-making efforts of the researched. In methodological textbooks on ethnography, immersion tends to be emphasized at the expense of its counterpart. In fact, ‘distancing’ is often ignored as a central tenet of good ethnographic conduct. In this article we redirect attention away from familiarization and towards ‘defamiliarization’ by suggesting six estrangement strategies (three theoretical and three methodological) that allow the researcher to develop a more detached viewpoint from which to interpret data. We demonstrate the workings of these strategies by giving illustrations from Machteld de Jong’s field- and text-work, conducted among Moroccan-Dutch students in an institution of higher vocational education.

Promoting explicit instruction of strategies for self-regulated learning

Using machine learning to understand students' gaze patterns on graphing tasks

Graphs are ubiquitous. Many graphs, including histograms, bar charts, and stacked dotplots, have proven tricky to interpret. Students’ gaze data can indicate students’ interpretation strategies on these graphs. We therefore explore the question: In what way can machine learning quantify differences in students’ gaze data when interpreting two near-identical histograms with graph tasks in between? Our work provides evidence that using machine learning in conjunction with gaze data can provide insight into how students analyze and interpret graphs. This approach also sheds light on the ways in which students may better understand a graph after first being presented with other graph types, including dotplots. We conclude with a model that can accurately differentiate between the first and second time a student solved near-identical histogram tasks.