The effect of graph standardization on intervention evaluation of practitioner‐created graphs

2021 ◽  
Author(s):  
Heather H. J. Lewis ◽  
Keith C Radley ◽  
Evan H. Dart
Keyword(s):  
Intervention Evaluation

Personalentwicklung und Training

2002 ◽  
Vol 1 (2) ◽  
pp. 59-79 ◽  
Author(s):  
Karlheinz Sonntag
Keyword(s):  
State Of The Art ◽  
Intervention Evaluation

Zusammenfassung. Führungskräfte und Mitarbeiter müssen kontinuierliche Anpassungsleistungen in immer kürzeren Zyklen erbringen, individuelle und kollektive Wissensbestände verändern sich, nicht automatisierbare Aufgaben werden komplexer und deren Bewältigung kognitiv anspruchsvoller. Diese Entwicklungen erfordern ein Ressourcenmanagement, das auf ständige Weiterentwicklung der beruflichen Handlungskompetenz ausgerichtet ist. Die Förderung und Entwicklung kompetenter Organisationsmitglieder mit dem Ziel der Wissensvermittlung, Verhaltensmodifikation und Persönlichkeitsentwicklung geschieht auf individueller, gruppenbezogener und organisationaler Ebene in vielfältiger Art und Weise und ist äußerst facettenreich. Eine zukunftsorientierte und wirksame Personalentwicklung ist ohne psychologisches Grundlagen- und Methodenwissen nicht mehr durchführbar. Der Beitrag leistet eine aktuelle Bestandsaufnahme deutscher und angloamerikanischer psychologischer Forschungsarbeiten zur Personalentwicklung. Die State of the Art-Analyse und Diskussion folgt dabei einem Phasenmodell, aufgeteilt nach Analyse, Intervention, Evaluation und Transfer.

Reversion Toward the Mean Independently of Regression Toward the Mean

Methodology ◽  
2009 ◽  
Vol 5 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Merton S. Krause
Keyword(s):  
Random Error ◽  
Evaluation Studies ◽  
Internal Validity ◽  
Intervention Evaluation ◽  
Population Mean ◽  
Experimental Intervention ◽  
Sample Data ◽  
The Mean ◽  
Regression Toward The Mean

There is another important artifactual contributor to the apparent improvement of persons subjected to an experimental intervention which may be mistaken for regression toward the mean. This is the phenomenon of random error and extreme selection, which does not at all involve the population regression of posttest on pretest scores but involves a quite different and independent reversion of subjects’ scores toward the population mean. These two independent threats to the internal validity of intervention evaluation studies, however, can be detected and differentiated on the sample data of such studies.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals The Use of Pointwise Encoding Time Reduction With Radial Acquisition MRA to Assess Middle Cerebral Artery Stenosis Pre- and Post-stent Angioplasty: Comparison With 3D Time-of-Flight MRA and DSA

2021 ◽  
Vol 8 ◽  
Author(s):  
Feifei Zhang ◽  
Yuncai Ran ◽  
Ming Zhu ◽  
Xiaowen Lei ◽  
Junxia Niu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Magnetic Resonance Angiography ◽  
Flow Visualization ◽  
Middle Cerebral Artery ◽  
Cerebral Artery ◽  
Time Of Flight ◽  
Intervention Evaluation ◽  
Tof Mra ◽  
Angioplasty And Stenting

Background and Purpose: 3D pointwise encoding time reduction magnetic resonance angiography (PETRA-MRA) is a promising non-contrast magnetic resonance angiography (MRA) technique for intracranial stenosis assessment but it has not been adequately validated against digital subtraction angiography (DSA) relative to 3D-time-of-flight (3D-TOF) MRA. The aim of this study was to compare PETRA-MRA and 3D-TOF-MRA using DSA as the reference standard for intracranial stenosis assessment before and after angioplasty and stenting in patients with middle cerebral artery (MCA) stenosis.Materials and Methods: Sixty-two patients with MCA stenosis (age 53 ± 12 years, 43 males) underwent MRA and DSA within a week for pre-intervention evaluation and 32 of them had intracranial angioplasty and stenting performed. The MRAs' image quality, flow visualization within the stents, and susceptibility artifact were graded on a 1–4 scale (1 = poor, 4 = excellent) independently by three radiologists. The degree of stenosis was measured by two radiologists independently on DSA and MRAs.Results: There was an excellent inter-observer agreement for stenosis assessment on PETRA-MRA, 3D-TOF-MRA, and DSA (ICCs > 0.90). For pre-intervention evaluation, PETRA-MRA had better image quality than 3D-TOF-MRA (3.87 ± 0.34 vs. 3.38 ± 0.65, P < 0.001), and PETRA-MRA had better agreement with DSA for stenosis measurements compared to 3D-TOF-MRA (r = 0.96 vs. r = 0.85). For post-intervention evaluation, PETRA-MRA had better image quality than 3D-TOF-MRA for in-stent flow visualization and susceptibility artifacts (3.34 ± 0.60 vs. 1.50 ± 0.76, P < 0.001; 3.31 ± 0.64 vs. 1.41 ± 0.61, P < 0.001, respectively), and better agreement with DSA for stenosis measurements than 3D-TOF-MRA (r = 0.90 vs. r = 0.26). 3D-TOF-MRA significantly overestimated the stenosis post-stenting compared to DSA (84.9 ± 19.7 vs. 39.3 ± 13.6%, p < 0.001) while PETRA-MRA didn't (40.6 ± 13.7 vs. 39.3 ± 13.6%, p = 0.18).Conclusions: PETRA-MRA is accurate and reproducible for quantifying MCA stenosis both pre- and post-stenting compared with DSA and performs better than 3D-TOF-MRA.

Abstract WP257: Accuracy of Pre-enrollment Last Known Well Time in a Large Prehospital Stroke Clinical Trial

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Nerses Sanossian ◽  
Emma Balouzian ◽  
Lucas Ramirez ◽  
David S Liebeskind ◽  
Sidney Starkman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Routine Practice ◽  
Stroke Therapy ◽  
Screening Criteria ◽  
Intervention Evaluation ◽  
Treatment Start ◽  
Tiered System ◽  
Telephone Screening ◽  
Entry Window

Background: Clinical trials of stroke therapies require accurate documentation of last known well time (LKWT) to account for injury accumulation prior to treatment start. For prehospital studies, this requirement is particularly challenging, as paramedic-determined and final-determined LKWTs in routine practice are concordant (within 15 mins) in only half of cases. We sought to determine the accuracy of LKWT obtained in the field by a two-tier enrollment system of study-trained paramedics and cellphone-connected physician-investigators. Methods: Paramedics screened consecutive transports for participation in the NIH Field Administration of Stroke Therapy-Magnesium (FAST-MAG) clinical trial. Paramedic screening criteria included LKWT <2 hours. Physician-investigators by cellphone confirmed or refined the LKWT after conversation with paramedics and patients or legally authorized representative. Prehospital LKWT was compared with post-arrival LKWT determined by trained study nurses after ED arrival by speaking with patients, family and other sources. We describe the number of enrollment calls with inaccurate LKWT at the paramedic-screening level and at the post paramedic plus physician-investigator telephone screening level. Results: A total of 4458 post-screening enrollment calls were made by paramedics from January 2005 to December 2012 of which 539 (12%) were determined by physician-instigators to have inaccurate LKWT leading to non-enrollment. Of the remaining 3919 calls, 1700 led to enrollments in the study and 2219 were not enrolled for a reason other than inaccurate LKWT. Among enrolled cases, exact congruence between prehospital and post-arrival LKWTs occurred 72% (n=1220), concordance within 15 minutes in 87%, within 30 minutes in 93%, and within 1 hour in 97%. Among enrolled cases, final-determined LKWT was within the study entry window of 2h in 96.3%. Conclusions: A 2-tiered system of paramedic screening followed by physician-investigator cellphone assessment led to high congruence between prehospital-determined and post-arrival-determined of LKWT. This system can be used in future trials of prehospital, paramedic-in initiated stroke therapy when accuracy of LKWT is important for intervention evaluation.

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