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Encounter Groups

Encounter Groups: A collaborative approach to personal growth and self-discovery.
These small group settings facilitate open communication, mutual understanding, and the exploration of interpersonal dynamics.
Participants engage in structured activities and discussions to enhance self-awareness, improve relationship skills, and foster personal development.
Encounter Groups are widely used in therapeutic, educational, and organizational settings to promote human potential and facilitate positive change.

Most cited protocols related to «Encounter Groups»

1
Buprenorphine Tapering Regimens for Opioid Dependence
Cited 19 times
This was a randomized, parallel-group, open-label study in which all procedures were identical for all participants until the taper period. After completing the consent process and baseline assessments, participants were inducted onto buprenorphine (Suboxone®) according to clinical practice, and stabilized on dose during the 4-week stabilization period. After the induction/stabilization phase, participants were assigned randomly to either a 7-day or 28-day tapering regimen (Table 1).
Participants completed weekly data collection to week 8, including the 4 induction/stabilization weeks and 4 successive weeks, so that participants in both conditions had the same number of data collection visits. Participants in both groups also attended clinic once weekly for 4 weeks after starting the taper (post-randomization) to maintain equivalence in the number of clinic visits across the two taper groups. For the 7-day taper group, medication provision ended at day 7, and medication provision extended to day 28 for the longer taper group. Follow-up interviews occurred at 1 month and 3 months post-taper.
The maximum length of study participation was approximately 5 months, including screening, induction/stabilization, taper and follow-up phases. Participants were encouraged to participate in the psychosocial treatment program administered at the treatment site (treatment as usual; TAU). Because this trial was designed to reflect real-world practice, no effort was made to standardize the psychosocial services or require participation in the psychosocial component. Following the taper, participants could continue in TAU or be referred to other local treatment resources.
The primary outcome was the percentage of participants in each taper group who were present and provided urine samples free of illicit opioids at the end of the taper period, and again at 1-month and 3-month follow-up assessments. Secondary outcomes included group comparisons of use of all drugs; withdrawal scores; the number of concomitant medications used to treat withdrawal symptoms; craving scores; and treatment satisfaction scores.
Ling W., Hillhouse M., Domier C., Doraimani G., Hunter J., Thomas C., Jenkins J., Hasson A., Annon J., Saxon A., Selzer J., Boverman J, & Bilangi R. (2009). Buprenorphine tapering schedule and illicit opioid use. Addiction (Abingdon, England), 104(2), 256-265.
Publication 2009
Buprenorphine Encounter Groups Opioids Pharmaceutical Preparations Satisfaction Suboxone Treatment Protocols Urine Withdrawal Symptoms
2
Mind-Body Approaches to Pain Trial
Cited 10 times
We previously published the Mind-Body Approaches to Pain (MAP) trial protocol [15 (link)]. The primary source of participants was Group Health (GH), a large integrated healthcare system in Washington State. Letters describing the trial and inviting participation were mailed to GH members who met the electronic medical record (EMR) inclusion/exclusion criteria, and to random samples of residents in communities served by GH. Individuals who responded to the invitations were screened and enrolled by telephone (Figure 1). Potential participants were told that they would be randomized to one of “two different widely-used pain self-management programs that have been found helpful for reducing pain and making it easier to carry out daily activities” or to continued usual care plus $50. Those assigned to MBSR or CBT were not informed of their treatment allocation until they attended the first session. We recruited participants from 6 cities in 10 separate waves.
We recruited individuals 20 to 70 years of age with non-specific low back pain persisting at least 3 months. Persons with back pain associated with a specific diagnosis (e.g., spinal stenosis), with compensation or litigation issues, who would have difficulty participating (e.g., unable to speak English, unable to attend classes at the scheduled time and location), or who rated pain bothersomeness <4 and/or pain interference with activities <3 on 0–10 scales were excluded. Inclusion and exclusion criteria were assessed using EMR data for the previous year (for GH enrollees) and screening interviews. Participants were enrolled between September 2012 and April 2014. Due to slow enrollment, after 99 participants were enrolled, we stopped excluding persons 64–70 years old, GH members without recent visits for back pain, and patients with sciatica. The trial protocol was approved by the GH Human Subjects Review Committee. All participants gave informed consent.
Cherkin D.C., Sherman K.J., Balderson B.H., Cook A.J., Anderson M.L., Hawkes R.J., Hansen K.E, & Turner J.A. (2016). Effects of Mindfulness-Based Stress Reduction vs Cognitive-Behavioral Therapy and Usual Care on Back Pain and Functional Limitations among Adults with Chronic Low Back Pain: A Randomized Clinical Trial. JAMA, 315(12), 1240-1249.
Publication 2016
Back Pain Diagnosis Encounter Groups Healthy Volunteers Human Body Litigation Management, Pain Pain Patients Respiratory Diaphragm Sciatica Self-Management Spinal Stenosis
3
Modeling Pioglitazone's Effects on Insulin Sensitivity
Cited 10 times
To model the effects of pioglitazone, we examined the outcome when insulin sensitivity was modeled to be in the liver, in the periphery, or at both sites equally—all with a standardized increase in β-cell function. Three possible sites of action on insulin sensitivity for pioglitazone were modeled: hypothesis 1, insulin sensitivity increases in both periphery and hepatic (variables L5 and PE5); hypothesis 2, hepatic insulin sensitivity increases and peripheral insulin sensitivity remains unchanged (variable L5); and hypothesis 3, hepatic insulin sensitivity remains unchanged and peripheral insulin sensitivity increases (variable PE5).
The development dataset comprised insulin and glucose values from a monotherapy study of pioglitazone (16 (link)). The changes to β-cell function and insulin sensitivity observed between the baseline visit and end of study (12 months later) in the development group were used as inputs to adjust the variables in the iHOMA2 model for each of the three hypotheses. A separate study of pioglitazone (17 (link)) was used for the verification group. The data from the baseline visits of the verification group were submitted to the adjusted iHOMA2, using the model in predictive mode, to determine the effect of pioglitazone for each of the three hypotheses using as output the expected fasting glucose and insulin after therapy. We assessed bias and agreement using a Bland-Altman plot and assessed the fit of the model by examination of the least squares deviation from the line of unity (where the observed values equal the predicted values) using an F statistic to test the model fit.
Hill N.R., Levy J.C, & Matthews D.R. (2013). Expansion of the Homeostasis Model Assessment of β-Cell Function and Insulin Resistance to Enable Clinical Trial Outcome Modeling Through the Interactive Adjustment of Physiology and Treatment Effects: iHOMA2. Diabetes Care, 36(8), 2324-2330.
Publication 2013
Encounter Groups Glucose Hyperinsulinism Hypersensitivity Insulin Insulin Sensitivity Liver Pancreatic beta Cells Physiology, Cell Pioglitazone Therapeutics
4
Antibiotic Prescribing Reduction Interventions
Cited 7 times
Our primary analysis was based on the approach of Gerber et al12 (link): we used a piecewise
hierarchical model with a knot at month 0 (the intervention start date) to model
trajectories of the log odds of the main outcome for the control group and each
intervention group, starting 18 months before each intervention began and ending
after 18 months of intervention exposure, and including random effects for
practices and clinicians. The estimates of interest were intervention ×
time interaction terms, which represented changes in prescribing trajectories,
relative to contemporaneous controls, that occurred when each intervention
began. This model measured the effects of each intervention in comparison with
all practices that did not receive the intervention, adjusting for exposure to
other interventions and practice-and clinician-level effects to account for
time-invariant characteristics (eg, specific EHR product used). We did not
adjust for patient characteristics, which were measured after clinicians were
exposed to the interventions and could be concomitant with outcome. Instead, we
relied on block randomization to equate groups on patient characteristics.
To display findings on the original scale of the data, we generated
monthly marginal predictions and confidence intervals from this model
corresponding to the control condition and each intervention individually.
Confidence intervals for differences between control and intervention (ie, for
intervention effects) were bootstrapped with 1000 replications.
We performed sensitivity analyses to test for interactions between
interventions by expanding the main effects model to include interaction terms
for each combination of interventions and comparing this fully interacted model
to the original main model using a Wald test.30 We also evaluated interaction terms
individually using a similar approach.
With an expected 2252 or more visits per intervention group, a priori
calculations indicated 80% power to detect a 7% absolute reduction in antibiotic
prescribing (less than the 8.9% median effect in prior efforts to improve
antibiotic quality improvement)8 at the .05 level of significance, assuming a baseline
prescribing rate of 50% and intrapractice correlation coefficient of 0.05. These
expected visit counts and correlation coefficients were based on preintervention
analyses, using data from the Boston and Los Angeles sites.
To investigate the possibility that interventions led to diagnosis
shifting (ie, changes in clinicians’ diagnostic coding habits), we used
the approach of our main models to test whether potentially
antibiotic-appropriate acute respiratory tract infection diagnoses (eg,
pneumonia, chronic sinusitis) increased as a proportion of all acute respiratory
tract infection diagnoses.31 (link)In sensitivity analyses, we fit a simple difference-indifferences model
estimating changes in the primary outcome associated with each intervention,
treating the entire 18-month intervention period as a binary variable, without
accounting for prescribing trajectories.
Elements of our analytic approach (specifically, using an 18-month
baseline period and piecewise hierarchical modeling technique in the main
analyses, performing the interaction effect sensitivity analysis, and performing
the simple difference-in-differences model sensitivity analyses) were modified
from our original analysis plan based on feedback received during the peer
review process.
Under the direction of an independent data and safety monitoring board,
we evaluated patient safety. For antibiotic-inappropriate visits in which no
antibiotic was prescribed, we assessed return visits within 30 days for the
presence of complications potentially attributable to untreated bacterial
infections (eTable 1 in
Supplement 2). We
conducted chart reviews on a 20% random sample of such cases to determine
whether prescription of antibiotics at the initial visit would have prevented
the complication.
We performed a complete case analysis. There were no missing values of
the main outcome, and we did not impute missing covariate values (which were
missing in approximately 3% of records). We analyzed data using Stata MP version
12.1 (StataCorp) and considered 2-sided P values less than .05
significant.
Meeker D., Linder J.A., Fox C.R., Friedberg M.W., Persell S.D., Goldstein N.J., Knight T.K., Hay J.W, & Doctor J.N. (2016). Effect of Behavioral Interventions on Inappropriate Antibiotic Prescribing Among Primary Care Practices: A Randomized Clinical Trial. JAMA, 315(6), 562-570.
Publication 2016
Antibiotics Clinical Trials Data Monitoring Committees Diagnosis DNA Replication Encounter Groups Hypersensitivity Infection Patients Patient Safety Pneumonia Respiratory Tract Infections Sinusitis
5
SEPA: HIV Risk Reduction for Hispanic Women
Cited 7 times
SEPA is an HIV risk reduction intervention for Hispanic women. SEPA’s conceptual framework integrates the social-cognitive model of behavioral change [24 ] and Freire’s pedagogy [26 ]. The social-cognitive model drove the content and activities of the session, which included structured activities that would promote self-efficacy (e.g., condom use demonstration, communication activities). Freire’s pedagogy drove the delivery and contextual tailoring of SEPA by establishing the importance of every individual in the group contributing to the knowledge and skills that were generated during the session and providing an atmosphere that encouraged participants to engage in discussion and activities. SEPA consisted of five, 2-h sessions delivered in small groups (M = 4.79 women, SD = 1.97); 163 (60%) women attended at least 1 session, with 119 (73%) of those attending all sessions. Sessions covered HIV/AIDS in the Hispanic community, STIs, HIV/AIDS prevention (e.g., condom use), negotiation and communication with the partner, IPV and substance abuse. Five bilingual and bicultural Hispanic female facilitators with a range of education (bachelors to doctoral) delivered the intervention. Groups were conducted in English or Spanish according to the language with which participants expressed they felt most comfortable. Groups took place in community sites easily accessible to participants. Role play, participatory sessions, videos and discussions were used to build skills. At the 6-month follow-up, women in SEPA were invited to a booster session to discuss topics related to the HIV intervention. In total, there were 14 booster sessions offered. A small proportion (n = 31, 11%) of the participants randomized to the intervention condition attended these boosters. The control group received a one-session, condensed version of SEPA after their 12-month assessment. Fidelity was ensured through a facilitator training, intervention manual and standardized PowerPoint presentations that would assist the facilitator in covering the content and activities during each session. The PI of the study also conducted unannounced visits to groups led by each of the facilitators to assess and address fidelity.
Peragallo N., Gonzalez-Guarda R.M., McCabe B.E, & Cianelli R. (2012). The Efficacy of an HIV Risk Reduction Intervention for Hispanic Women. AIDS and behavior, 16(5), 1316-1326.
Publication 2012
Acquired Immunodeficiency Syndrome Atmosphere Condoms Encounter Groups Feelings Females Hispanic or Latino Hispanics Obstetric Delivery Physicians Secondary Immunization Sexually Transmitted Diseases Substance Abuse Woman

Most recents protocols related to «Encounter Groups»

1
Neck-Specific Rehabilitation for Chronic Whiplash
This study is part of a larger research project with a multi-center RCT, aiming
to compare two different ways of distributing neck-specific rehabilitation to
individuals with chronic WAD in primary health care in Sweden.8 (link) After
written and oral informed consent, 140 individuals with chronic neck problems
corresponding to WAD grades 2–318 (link) verified by clinical
examination, were included in the RCT and randomized into one of two groups.
Both groups received the same NSE for 12 weeks. Exercises were chosen from a
clear and written frame and included exercises for the deep neck muscles,
continuing with the endurance, training of neck and shoulder muscles. Part 1:
Activation by supine isometric exercises 5 repetitions 5 times a day with
progression to next part. Part 2: Progression from supine to sitting isometric
exercises 3 × 10, 3 times a day. Part 3: Endurance training, starting with 5–10
repetitions and progression to 3 × 20 if tolerated, 3 times a week. Training of
neck and shoulder muscles were included, 3 × 10, 3 times a week. The exercises
are individually adjusted according to the individual's physical conditions and
progressively increased in severity and dose. These exercises have been used
with good results in a previous RCT, where the program is described more in
detail.19 (link),20 (link) The first visit to the physiotherapist take
approximately 60 minutes and the others 30 minutes. Group A received
internet-based support in combination with four visits to the physiotherapist
while group B received two visits/week to the physiotherapist but without the
internet-based support.8 (link) This internet-based support consisted of a digital
platform with information provided in text, pictures and videos about pain, pain
management, WAD, neck muscle function, how to perform the NSE, as well as an
opportunity to report daily exercises and receive SMS reminders to do the
exercises. No tools or special aids were provided for the internet-based
support.
Physiotherapists who had experienced at least one patient randomized to the
internet-based support group were eligible to participate in this focus group
study. To facilitate group discussions, we strived for a variation of
physiotherapists regarding age, gender, length of work experience, working in
public and private primary care centers and different regions in south and
central Sweden. Eligible physiotherapists were approached by e-mail with
information of the study, and interested physiotherapists were given extended
written and oral information. Nine physiotherapists chose to participate, and
were strategically distributed into one of the focus groups to reach a variation
of experiences (different regions, public/private clinic, age, etc.). Three
focus group discussions were held with three physiotherapists in each group.
Participant characteristics are shown in Table 1.
Nilsing Strid E., Gustafson F., Peolsson A, & Hermansen A. (2023). Physiotherapists’ experiences of internet-based neck-specific exercises for patients with chronic whiplash-associated disorders. Digital Health, 9, 20552076231159181.
Publication 2023
Acquired Immunodeficiency Syndrome ARID1A protein, human Disease Progression Encounter Groups Exercise, Isometric Gender Muscle Tissue Neck Neck Muscles Pain Patients Physical Examination Physical Therapist Primary Health Care Reading Frames Rehabilitation Shoulder
2
Evaluating Continuous Glucose Monitoring Outcomes
A sample size of 216 participants was determined to detect a between-group difference in the target range (3.9–10 mmol/L), assuming a significant difference of an α-level of 0.05, power of 80% (β = 0.2), and a SD of 14. This number was increased to 270 participants to account for 20% with missing follow-up data.
All participants were analyzed according to their randomization group and included in the primary analysis. For the primary analysis, differences in the primary and secondary CGM outcomes between the final visit and screening visit in the two groups were assessed using paired t-tests. Missing data were managed with the direct likelihood method, which maximizes the likelihood function integrated over possible values of the missing data.
Analyses of prespecified secondary outcomes were conducted in parallel with the analysis of the primary outcome (CGM data were pooled across follow-up time points). Analysis of covariance was used to adjust for chance imbalances in baseline measurements between the treatment groups. Modification of the treatment effect by baseline variables was assessed by including an interaction term in the primary model. Secondary outcomes were analyzed by analysis of covariance of the differences between post-baseline and baseline values with study center, diabetes duration, baseline BMI, baseline SD, and baseline HbA1c as covariates in the two groups. Confidence intervals were calculated for the group least-square mean of each measure and the difference between group least-square means. Two-sided statistical tests were performed, and a significance of 0.05 was used in all tests.
The results were reported as the mean ± SD [minimum, maximum] or documented as the constituent ratio. Analyses were conducted with the SPSS 23.0 software.
Guo L., Li Y., Zhang M., Xiao X., Kuang H., Yang T., Jia X, & Zhang X. (2023). Efficacy of unblinded and blinded intermittently scanned continuous glucose monitoring for glycemic control in adults with type 1 diabetes. Frontiers in Endocrinology, 14, 1110845.
Publication 2023
Diabetes Mellitus Encounter Groups
3
Exploring Atogepant for Migraine Treatment
All analyses were conducted on participants in the modified intent-to-treat (mITT) population with available data for each outcome measure and time point. The mITT population included all participants who received ≥1 dose of study drug, who had an evaluable baseline period of eDiary data, and who had ≥1 evaluable postbaseline 4-week period of eDiary data during the double-blind treatment period. Descriptive comparisons between the atogepant and placebo groups were reported as differences in change from baseline and corresponding odds ratios. Secondary endpoints of change from baseline in MSQ v2.1 RFR domain score at week 12 and changes from baseline in mean monthly AIM-D domain scores across the 12-week treatment period were analyzed using mixed models for repeated measures (MMRMs) that included the treatment group, visit, previous exposure to a migraine prevention medication (yes/no), and treatment group by visit as categorical fixed effects and baseline score and baseline-by-visit interaction as covariates. The overall type I error rate for multiple comparisons across the 3 atogepant doses and the secondary efficacy endpoints was controlled at the 0.05 level using a graphical approach with weighted-Bonferroni test procedure.34 (link) Within each dose, testing started from the primary endpoint, followed by testing of the secondary endpoints in a prespecified order.18 (link)Continuous exploratory endpoints (i.e., changes from baseline in MSQ v2.1 domain scores, mean monthly AIM-D domain and total scores, and HIT-6 total scores) were analyzed using MMRM models that included treatment group, visit, previous exposure to a preventive migraine medication (yes/no), and treatment group by visit as categorical fixed effects; and baseline score and baseline-by-visit interaction as covariates. Binary exploratory endpoints (i.e., percentage of participants with ≥5-point improvement [decrease] from baseline in HIT-6 total score) were analyzed using a generalized linear mixed model that included treatment group, visit, previous exposure (yes/no) to a preventive migraine medication, and treatment group–by-visit interaction as categorical fixed effects; baseline value and baseline-by-visit interaction as covariates; and participants as random effects. Post hoc exploratory analyses of the proportions of participants reaching the within-group MID for each MSQ v2.1 domain used a similar generalized linear mixed model. p Values from the tests between each atogepant dose group and the placebo group are reported. Except for secondary efficacy endpoints, all analyses were performed at the nominal significance level, without adjusting for multiplicity.
Lipton R.B., Pozo-Rosich P., Blumenfeld A.M., Li Y., Severt L., Stokes J.T., Creutz L., Gandhi P, & Dodick D. (2023). Effect of Atogepant for Preventive Migraine Treatment on Patient-Reported Outcomes in the Randomized, Double-blind, Phase 3 ADVANCE Trial. Neurology, 100(8), e764-e777.
Publication 2023
atogepant Encounter Groups Migraine Disorders Pharmaceutical Preparations Placebos
4
Analyzing Treatment Access Barriers in RA
Data were summarized by descriptive statistics. Univariate analyses were performed on the patients' demographic and clinical characteristics regarding RA status and comorbidities in both groups (access barriers and without access barriers). For continuous variable, t test was used when it has a normal distribution or Wilcoxon Rank‐Sum test for nonparametric distribution. In categorical variables, X2 test and Kruskal−Wallis test depending on the number of categories. The statistical software package R (version 4.0.5) was used to conduct the statistical analysis.
The clinical outcomes were analyzed by estimating the difference in means using least square means between periods and patients that reported access barriers, interruption, or TtS higher than the mean. Therefore, a bivariate and multivariable analysis was performed to identify the association between potential confounding variables.
In the difference between visits in each PROs, linear regression was conducted for the multivariable analysis. The adjusted full model was composed of all potential confounding variables such as demographic and clinical characteristics, concomitant treatment, treated with tofacitinib or bDMARD, among others (e.g., age, gender, country of origin, previous treatments, neutrophils, insurance, and baseline clinical data such as DAS28‐ESR). The reduced model was developed from the results of multivariable analysis selecting the variables with p value less than 0.05, it was considered as statistically significant. For comparison between PRO's score in the baseline and 6‐month visit for each studied groups were used paired t‐test for unadjusted analysis and mixed effects regression analysis for adjusted results.
The changes in the clinical outcomes during the period of follow‐up were expressed as the least mean difference, and variability measures were standard deviation (SD) and standard error (SE). Multiple imputation was used to manage the missing data from the different variables using multiple imputation methods Multivariate Imputation by Chained Equations (MICE) with five imputations using the predictive mean matching based all the variables available to conduct the prediction (outcomes and treatment were not used as predictors). It was run with the package MICE in R software (version 4.0.5).
Reyes J.M., Gutierrez‐Ardila M.V., Madariaga H., Otero W., Guzman R., Izquierdo J., Del Castillo D.J., Abello M., Velez P., Ponce de Leon D., Lukic T., Amador L.F, & Castaño N. (2023). Impact of access to treatment on patient‐reported outcomes among rheumatoid arthritis patients with tDMARDs and bDMARDS in two Latin‐American countries: A prospective observational study. Health Science Reports, 6(3), e1034.
Publication 2023
Birth Encounter Groups Gender Neutrophil Patients tofacitinib
5
Depression Diagnosis Characteristics and Outcomes
We classified patients’ characteristics at the time of depression diagnosis into the following categories: age group (10–14 years, 15–19 years), sex (male or female according to biological sex), race and ethnicity (Black, Hispanic, White, or other/missing [other race and ethnicity includes American Indian or Alaskan Native, Asian, Native Hawaiian or Other Pacific Islander, or multiracial]), and insurance type (public, private, or other/missing). Because either race or ethnicity, but not both, are often listed in Explorys, when Hispanic ethnicity was specified, it took precedence over race. ‘Missing’ was combined with ‘other’ for the race and ethnicity and insurance variables in statistical modeling due to small numbers in some violence encounter categories. Race and ethnicity were analyzed as a covariate because it may be a confounder for the association of violence encounters and suicidal ideation.
Preexisting substance use was defined as documentation of alcohol use, tobacco use, or other substance use at any time before the index date. Alcohol use and smoking status were identified according to patient self-reported behavior or clinician-based ICD-10-CM diagnosis. ICD-10-CM diagnoses were used to identify other substance use. As substance use may be a sequela of violence encounters11 (link)—and thus is regarded as a mediator—substance use documented after the most recent violence encounter was not included as a covariate for the encounter group to avoid overadjusting for the effects of substance use.
Mental illness was defined as any diagnosis of autism spectrum disorder, attention-deficit/hyperactivity disorder, conduct disorder, or schizophrenia before the index date and was treated as confounders associated with violence encounters18 (link) and suicidal ideation.19 (link),20 (link) Anxiety, often a sequela of violence encounters,11 (link) was considered a mediator and therefore was not adjusted for in the model. Detailed algorithms to define all conditions are presented in eTable 1 in Supplement 1. The specified timings relative to the index date in the definitions of each condition are summarized in eTable 2 in Supplement 1.
Wang J., Harrer S., Zwald M.L., Leemis R.W., Holland K.M., Stone D.M., McDavid Harrison K, & Swedo E.A. (2023). Association of Recent Violence Encounters With Suicidal Ideation Among Adolescents With Depression. JAMA Network Open, 6(3), e231190.
Publication 2023
Age Groups American Indians Anxiety Disorders Asian Persons Autism Spectrum Disorders Biopharmaceuticals Conduct Disorder Diagnosis Dietary Supplements Disorder, Attention Deficit-Hyperactivity Encounter Groups Ethnicity Females Hispanics Males Mental Disorders Native Hawaiians Pacific Islander Americans Patients Schizophrenia Substance Use

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More about "Encounter Groups"

Encounter Groups are small, collaborative settings that facilitate personal growth, self-discovery, and interpersonal development.
These group-based interventions, also known as T-Groups or Therapy Groups, encourage open communication, mutual understanding, and the exploration of group dynamics.
Participants engage in structured activities and discussions to enhance self-awareness, improve relationship skills, and foster positive change.
Encounter Groups have been widely used in therapeutic, educational, and organizational settings to promote human potential and facilitate personal transformation.
These group-based interventions are similar to other group-based approaches, such as Support Groups, Discussion Groups, and Group Counseling, which may utilize various software tools like SAS version 9.4, Stata 13, SPSS version 21, and SPSS Statistics.
The collaborative and self-reflective nature of Encounter Groups can help individuals develop a deeper understanding of themselves and their interpersonal relationships.
Through the process of group interaction and feedback, participants can learn to better manage their emotions, improve their communication skills, and cultivate more fulfilling personal and professional relationships.
Encounter Groups have been shown to be effective in a variety of contexts, including mental health treatment, corporate team-building, and educational settings.
Researchers and practitioners have explored the use of Encounter Groups in conjunction with statistical software like SAS 9.4, Stata 15, and SPSS version 18.0 to analyze the outcomes and effectiveness of these group-based interventions.