An evaluation of learning clinical decision-making for early rehabilitation in the ICU via interactive education with audience response system

Toonstra, Amy; Nelliot, Archana; Friedman, Lisa Aronson; Zanni, Jennifer M.; Hodgson, Carol

doi:10.1080/09638288.2016.1186751

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…Physicians may be more prone to have weaning as a target [ 26 ]. Beside these psychosocial and cultural aspects, other facets, such as working habits, inter-professional communication, staff-to-patient ratios, and equipment might contribute to the diverse decision-making [ 23 , 29 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

“…The assessment was based on a previous evaluation using patient scenarios [ 23 ], and was reported in accordance with recommendations for the conduction of online surveys [ 25 ]. All participants were informed about the study and its voluntary, anonymous approach, as well as the approximate time required to complete the questionnaire.…”

Section: Methodsmentioning

confidence: 99%

“…Interestingly, different professions report different barriers to early mobilization, e.g. nurses often report hemodynamic instability on continuous renal replacement therapy as barriers, while physical therapists report neurological impairments or inability to follow commands [16][17][18][19][20][21][22][23][24]. Hence, different professions may estimate the mobility of ICU patients differently.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of mobilization capacity in 10 different ICU scenarios by different professions

Hermes¹,

Nydahl

Blobner

et al. 2020

PLoS ONE

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Background Mobilization of intensive care patients is a multi-professional task. Aim of this study was to explore how different professions working at Intensive Care Units (ICU) estimate the mobility capacity using the ICU Mobility Score in 10 different scenarios. Methods T en fictitious patient-scenarios and guideline-related knowledge were assessed using an online survey. Critical care team members in German-speaking countries were invited to participate. All datasets including professional data and at least one scenario were analyzed. Kruskal Wallis test was used for the individual scenarios, while a linear mixed-model was used over all responses. Results In total, 515 of 788 (65%) participants could be evaluated. Physicians (p = 0.001) and nurses (p = 0.002) selected a lower ICU Mobility Score (-0.7 95% CI -1.1 to -0.3 and -0.4 95% CI -0.7 to -0.2, respectively) than physical therapists, while other specialists did not (p = 0.81). Participants who classified themselves as experts or could define early mobilization in accordance to the “S2e guideline: positioning and early mobilisation in prophylaxis or therapy of pulmonary disorders” correctly selected higher mobilization levels (0.2 95% CI 0.0 to 0.4, p = 0.049 and 0.3 95% CI 0.1 to 0.5, p = 0.002, respectively). Conclusion Different professions scored the mobilization capacity of patients differently, with nurses and physicians estimating significantly lower capacity than physical therapists. The exact knowledge of guidelines and recommendations, such as the definition of early mobilization, independently lead to a higher score. Interprofessional education, interprofessional rounds and mobilization activities could further enhance knowledge and practice of mobilization in the critical care team.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of mobilization capacity in 10 different ICU scenarios by different professions

Hermes¹,

Nydahl

Blobner

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

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“…In one of the first studies that employed this method, researchers used an ARS to provide information on anticoagulants to clinicians [4]. More recently, Tar-Ching et al [5] used an ARS to gather expert opinions on the barriers and priorities related to occupational health, and Toonstra et al [6] used an ARS to educate a diverse group of health care professionals on the safe rehabilitation of patients who were in intensive care units. Thus, ARSs have been used successfully for both educating health care professionals and gathering input from expert stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Audience Response Systems and Missingness Trends: Using Interactive Polling Systems to Gather Sensitive Health Information From Youth

Toscos¹,

Drouin²,

Flanagan³

et al. 2019

JMIR Form Res

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Background The widespread availability and cost-effectiveness of new-wave software-based audience response systems (ARSs) have expanded the possibilities of collecting health data from hard-to-reach populations, including youth. However, with all survey methods, biases in the data may exist because of participant nonresponse. Objective The aims of this study were to (1) examine the extent to which an ARS could be used to gather health information from youths within a large-group school setting and (2) examine individual- and survey-level response biases stemming from this Web-based data collection method. Methods We used an ARS to deliver a mental health survey to 3418 youths in 4 high schools in the Midwestern United States. The survey contained demographic questions, depression, anxiety, and suicidality screeners, and questions about their use of offline resources (eg, parents, peers, and counselors) and Web-based resources (ie, telemental health technologies) when they faced stressful life situations. We then examined the response rates for each survey item, focusing on the individual- and survey-level characteristics that related to nonresponse. Results Overall, 25.39% (868/3418) of youths answered all 38 survey questions; however, missingness analyses showed that there were some survey structure factors that led to higher rates of nonresponse (eg, questions at the end of survey, sensitive questions, and questions for which precise answers were difficult to provide). There were also some personal characteristics that were associated with nonresponse (eg, not identifying as either male or female, nonwhite ethnicity, and higher levels of depression). Specifically, a multivariate model showed that male students and students who reported their gender as other had significantly higher numbers of missed items compared with female students ( B =.30 and B =.47, respectively, P <.001). Similarly, nonwhite race ( B =.39, P <.001) and higher depression scores ( B =.39, P <.001) were positively related to the number of missing survey responses. Conclusions Although our methodology-focused study showed that it is possible to gather sensitive mental health data from youths in large groups using ARSs, we also suggest that these nonresponse patterns need to be considered and controlled for when using ARSs for gathering population health data.

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“…1,2 Medical sciences students should be read and translate specialized textbooks and journals and they need to learn and understand scientific language very well. 3,4 Some studies have shown the effectiveness of educational methods on students' learning. [5][6][7] One educational method often recommended is small group discussion learning or small group learning for students.…”

Section: Introductionmentioning

confidence: 99%

Small Group Learning for Understanding the Specialized Language in students of occupational Health

Assadi¹

2017

Res Dev Med Edu

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An evaluation of learning clinical decision-making for early rehabilitation in the ICU via interactive education with audience response system

Cited by 8 publications

References 10 publications

Assessment of mobilization capacity in 10 different ICU scenarios by different professions

Assessment of mobilization capacity in 10 different ICU scenarios by different professions

Audience Response Systems and Missingness Trends: Using Interactive Polling Systems to Gather Sensitive Health Information From Youth

Small Group Learning for Understanding the Specialized Language in students of occupational Health

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