scholarly journals Model structure for protocol adherence utilizing a manualized therapeutic massage intervention

2018 ◽  
Vol 16 (2) ◽  
Author(s):  
Erika R. Larson ◽  
Becky Kinkead ◽  
Sherry A. Edwards ◽  
Pamela J. Schettler ◽  
Boadie W. Dunlop ◽  
...  
Keyword(s):  
Early Phase ◽  
Assessment Model ◽  
Future Research ◽  
Massage Therapist ◽  
Cancer Related Fatigue ◽  
Monitoring Model ◽  
Phase Study ◽  
Massage Therapists ◽  
High Level ◽  
Study Participants

Abstract Background The Protocol Training and Assessment Model (Model) was developed through collaboration between Emory University School of Medicine and Atlanta School of Massage to minimize intra- and inter-therapist variability for two research massage therapist (rMT) applied intervention arms in the Massage for Cancer-Related Fatigue (MCRF) early-phase study. The Model was followed to maintain and assess protocol integrity for the study’s manualized Swedish massage therapy (SMT) and light touch (LT) interventions. Methods The Model includes initial rMT training, quarterly retraining sessions, accessible resources (scripts, treatment guides, weekly research personnel meetings), and ongoing monitoring. Model efficacy was assessed by monitoring data collected at retraining sessions, through audio recording review, and through subject and rMT reporting. Results Model application resulted in a high level of intervention consistency throughout the study. Protocol-related session comment rate by subjects was 2.7%. Few study participants reported intra-rMT or inter-rMT treatment delivery differences. Observation during retraining sessions indicated massage therapists continued to adhere to protocols. Importantly rMTs increased their participation beyond core duties, suggesting additional ways to standardize subject treatment experience. Conclusions Through systematic application of the Protocol Training and Assessment Model, continuous and collaborative quality improvement discussions between scientists and research massage therapists resulted in reliable, standardized SMT and LT interventions for the MCRF early-phase study. Future research can apply the Model to support and assess consistent rMT-delivered intervention applications.

scholarly journals Data-Driven Assessment Modeling 
of Partnership Data

2021 ◽  
Author(s):  
Matthew Seto ◽  
Kristin Medlin
Keyword(s):  
Community Engagement ◽  
High Strength ◽  
Assessment Model ◽  
Qualitative Assessment ◽  
Future Research ◽  
Institutional Research ◽  
University Partnerships ◽  
Team Members ◽  
Data Points ◽  
High Level

What does it mean to be in a strong partnership? Using Collaboratory’s national dataset of community engagement data, we explored partnerships between higher education institutions and the community organizations with which they are partnered. Our goals were to 1- understand what quantitative characteristics from Collaboratory denote ‘strong’ community-university partnerships, 2- use those characteristics to create an algorithmic assessment model to identify the strongest partnerships in the Collaboratory dataset, and 3- reveal common themes that practitioners can leverage to cultivate stronger and more resilient partnerships. With input from Collaboratory administrators, community engagement professionals, and institutional research team members, we identified four quantitative data points in Collaboratory data that we combined into a partnership strength model. The model identified 99 out of 2,083 community-university partnerships that might be classified as high-strength. The model’s results represent an initial jumping-off point for future research, including qualitative assessment of the 99 strongest partnerships to validate the model. Additionally, we argue that quantitative assessment of qualitative partnerships is by no means a silver bullet, but instead represents a pragmatic method of high-level assessment and quick filtering of large datasets of qualitative partnership data that would otherwise be prohibitively time-consuming.

“I am a healthcare practitioner”: A qualitative exploration of massage therapists’ professional identity

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Amanda Baskwill ◽  
Meredith Vanstone ◽  
Del Harnish ◽  
Kelly Dore
Keyword(s):  
Professional Identity ◽  
Service Providers ◽  
Patient Empowerment ◽  
Healthcare Providers ◽  
Qualitative Description ◽  
Future Research ◽  
Structured Interviews ◽  
Massage Therapist ◽  
Individualized Care ◽  
Massage Therapists

AbstractBackgroundA division has been described among massage therapists, some who identify as healthcare providers while others identify as service providers. The perceived division creates confusion about what it means to be a massage therapist.ObjectiveThis qualitative study answered, “How do massage therapists in Ontario describe their professional identity?”MethodsQualitative description (QD) was used and data were collected from 33 massage therapists using semi-structured interviews.ResultsThe resulting description of massage therapists’ identity in Ontario is the first of its kind. The identity described includes passion as professional motivation in practice, the importance of confidence and competence, a focus on the therapeutic relationship, individualized care, and patient empowerment, and a desire to be recognized for their role within the healthcare system.ConclusionThere is still much to be investigated about massage therapists’ identity. Future research will explore whether this description resonates with a larger sample of massage therapists in Ontario.

The Relationship Between Early-Phase Substance-Use Trajectories and Drug Court Outcomes

2011 ◽  
Vol 38 (9) ◽  
pp. 913-933 ◽  
Author(s):  
Craig G.A. Jones ◽  
Richard I. Kemp
Keyword(s):  
Substance Use ◽  
Early Phase ◽  
Drug Court ◽  
New South ◽  
Positive Test ◽  
Future Research ◽  
Membership Probability ◽  
South Wales ◽  
High Level ◽  
The Relationship

This study sought to identify patterns of substance use among 1,019 participants of the New South Wales Drug Court program (Sydney, Australia) between 2003 and 2009. Group-based trajectory modeling identified five groups of participants: compliant participants (24.4%), who had a near-zero probability of returning a positive urine test at each occasion; responding participants (25.3%), for whom the probability of returning a positive test decreased; relapsing participants (14.1%), for whom the probability of returning a positive test increased; mid-level chronic participants (26.0%), who had a one in two chance of returning a positive test at each episode; and a high-level chronic group (10.2%), who had a very high probability of returning a positive test at each episode. Group membership probability was found to be a good predictor of treatment and criminal justice outcomes. The challenge for future research is to identify the characteristics that explain these early-phase substance use trajectories.

Speech-Language Pathologists' Comfort Level With Use of Term “Stuttering” During Evaluations

2020 ◽  
Vol 29 (2) ◽  
pp. 841-850 ◽  
Author(s):  
Courtney T. Byrd ◽  
Danielle Werle ◽  
Kenneth O. St. Louis
Keyword(s):  
Communication Disorders ◽  
Academic Experiences ◽  
Comfort Level ◽  
Future Research ◽  
Clinical Experiences ◽  
Speech Language Pathologists ◽  
Comfort Levels ◽  
Parents And Children ◽  
Evaluation Feedback ◽  
Study Participants

Purpose Speech-language pathologists (SLPs) anecdotally report concern that their interactions with a child who stutters, including even the use of the term “stuttering,” might contribute to negative affective, behavioral, and cognitive consequences. This study investigated SLPs' comfort in providing a diagnosis of “stuttering” to children's parents/caregivers, as compared to other commonly diagnosed developmental communication disorders. Method One hundred forty-one school-based SLPs participated in this study. Participants were randomly assigned to one of two vignettes detailing an evaluation feedback session. Then, participants rated their level of comfort disclosing diagnostic terms to parents/caregivers. Participants provided rationale for their ratings and answered various questions regarding academic and clinical experiences to identify factors that may have influenced ratings. Results SLPs were significantly less likely to feel comfortable using the term “stuttering” compared to other communication disorders. Thematic responses revealed increased experience with a specific speech-language population was related to higher comfort levels with using its diagnostic term. Additionally, knowing a person who stutters predicted greater comfort levels as compared to other clinical and academic experiences. Conclusions SLPs were significantly less comfortable relaying the diagnosis “stuttering” to families compared to other speech-language diagnoses. Given the potential deleterious effects of avoidance of this term for both parents and children who stutter, future research should explore whether increased exposure to persons who stutter of all ages systematically improves comfort level with the use of this term.

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

Methodology ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Repeated Measures ◽  
Statistical Power ◽  
Mixed Model ◽  
Covariance Structure ◽  
Simulation Method ◽  
Future Research ◽  
Repeated Measures Design ◽  
Fixed And Random Effects ◽  
Split Plot ◽  
High Level

Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

scholarly journals Opportunities and Challenges of Green-Lean: An Integrated System for Sustainable Construction

2020 ◽  
Vol 12 (11) ◽  
pp. 4460 ◽  
Author(s):  
Mohammadsoroush Tafazzoli ◽  
Ehsan Mousavi ◽  
Sharareh Kermanshachi
Keyword(s):  
Cost Savings ◽  
Integrated Approach ◽  
Integrated System ◽  
Waste Minimization ◽  
Sustainable Construction ◽  
Future Research ◽  
Safety Improvement ◽  
The Individual ◽  
High Level ◽  
Industry Professionals

Although the two concepts of lean and sustainable construction have been developed due to different incentives, and they do not pursue the same exact goals, there exists considerable commonality between them. This paper discusses the potentials for integrating the two approaches and their practices and how the resulting synergy from combining the two methods can potentially lead to higher levels of fulfilling the individual goals of each of them. Some limitations and challenges to implementing the integrated approach are also discussed. Based on a comprehensive review of existing papers related to sustainable and lean construction topics, the commonality between the two approaches is discussed and grouped in five categories of (1) cost savings, (2) waste minimization, (3) Jobsite safety improvement, (4) reduced energy consumption, and (5) customers’ satisfaction improvement. The challenges of this integration are similarly identified and discussed in the four main categories of (1) additional initial costs to the project, (2) difficulty of providing specialized expertise, (3) contractors’ unwillingness to adopt the additional requirements, and (4) challenges to establish a high level of teamwork. Industry professionals were then interviewed to rank the elements in each of the two categories of opportunities and challenges. The results of the study highlight how future research can pursue the development of a new Green-Lean approach by investing in the communalities and meeting the challenges of this integration.

scholarly journals Time Granularity Impact on Propagation of Disruptions in a System-of-Systems Simulation of Infrastructure and Business Networks

2021 ◽  
Vol 18 (8) ◽  
pp. 3922
Author(s):  
Mateusz Iwo Dubaniowski ◽  
Hans Rudolf Heinimann
Keyword(s):  
Recovery Time ◽  
System Of Systems ◽  
Future Research ◽  
Business Networks ◽  
Simulation Experiments ◽  
Huge Impact ◽  
High Level ◽  
Systems Simulation ◽  
Different Characteristics ◽  
The Impact

A system-of-systems (SoS) approach is often used for simulating disruptions to business and infrastructure system networks allowing for integration of several models into one simulation. However, the integration is frequently challenging as each system is designed individually with different characteristics, such as time granularity. Understanding the impact of time granularity on propagation of disruptions between businesses and infrastructure systems and finding the appropriate granularity for the SoS simulation remain as major challenges. To tackle these, we explore how time granularity, recovery time, and disruption size affect the propagation of disruptions between constituent systems of an SoS simulation. To address this issue, we developed a high level architecture (HLA) simulation of three networks and performed a series of simulation experiments. Our results revealed that time granularity and especially recovery time have huge impact on propagation of disruptions. Consequently, we developed a model for selecting an appropriate time granularity for an SoS simulation based on expected recovery time. Our simulation experiments show that time granularity should be less than 1.13 of expected recovery time. We identified some areas for future research centered around extending the experimental factors space.

373 Historical Use of Substances for Insomnia Impacts Current Beliefs about Hypnotic Medications

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A148-A149
Author(s):  
Jessica Dietch ◽  
Norah Simpson ◽  
Joshua Tutek ◽  
Isabelle Tully ◽  
Elizabeth Rangel ◽  
...  
Keyword(s):  
Prescription Medication ◽  
Stepped Care ◽  
Future Research ◽  
Prescription Medications ◽  
Over The Counter ◽  
Multiple Treatment ◽  
History Of ◽  
Study Participants ◽  
Beliefs About Medications ◽  
The Impact

Abstract Introduction The purpose of the current study was to examine the relationship between current beliefs about hypnotic medications and historical use of prescription hypnotic medications or non-prescription substances for sleep (i.e., over the counter [OTC] medications, alcohol, and cannabis). Methods Participants were 142 middle age and older adults with insomnia (M age = 62.9 [SD = 8.1]; 71.1% female) enrolled in the RCT of the Effectiveness of Stepped-Care Sleep Therapy In General Practice (RESTING) study. Participants reported on history of substances they have tried for insomnia and completed the Beliefs about Medications Questionnaire-Specific with two subscales assessing beliefs about 1) the necessity for hypnotics, and 2) concerns about potential adverse consequences of hypnotics. Participants were grouped based on whether they had used no substances for sleep (No Subs, 11.6%), only prescription medications (Rx Only, 9.5%), only non-prescription substances (NonRx Only, 26.6%), or both prescription and non-prescription substances (Both, 52.3%). Results Sixty-one percent of the sample had used prescription medication for sleep and 79% had used non-prescription substances (74% OTC medication, 23% alcohol, 34% cannabis). The greater number of historical substances endorsed, the stronger the beliefs about necessity of hypnotics, F(1,140)=23.3, p<.001, but not about concerns. Substance groups differed significantly on necessity beliefs, F(3,1)=10.68, p<.001; post-hocs revealed the Both group had stronger beliefs than the No and NonRx Only groups. Substance groups also differed significantly on the concerns subscale, F(3,1)=6.68, p<.001; post-hocs revealed the NonRx Only group had stronger harm beliefs than the other three groups. Conclusion The majority of the sample had used both prescription and non-prescription substances to treat insomnia. Historical use of substances for treating insomnia was associated with current beliefs about hypnotics. Individuals who had used both prescription and non-prescription substances for sleep in the past had stronger beliefs about needing hypnotics to sleep at present, which may reflect a pattern of multiple treatment failures. Individuals who had only tried non-prescription substances for sleep may have specifically sought alternative substances due to concerns about using hypnotics. Future research should seek to understand the impact of treatment history on engagement in and benefit from non-medication-based treatment for insomnia. Support (if any) 1R01AG057500; 2T32MH019938-26A1

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