A Client-Centered Acceptance Intervention Model in Counseling Relationship: Focus on Conditions of Providing Acceptance Experience

2021 ◽  
Vol 8 (6) ◽  
pp. 77-97
Author(s):  
Seo-Won SHIN ◽  
Eun-Yi CHO ◽  
Ji-Won OH
Keyword(s):  
Intervention Model ◽  
Counseling Relationship

Promoting the Community's Ability to Detect and Respond to Suicide Risk Through an Online Bystander Intervention Model-Informed Tool

Crisis ◽  
2020 ◽  
pp. 1-7
Author(s):  
Karien Hill ◽  
Shawn Somerset ◽  
Ralf Schwarzer ◽  
Carina Chan
Keyword(s):  
Suicide Prevention ◽  
Suicide Risk ◽  
Controlled Trial ◽  
Intervention Group ◽  
Bystander Intervention ◽  
Self Report ◽  
Innovative Technology ◽  
Control Group ◽  
Intervention Model ◽  
Standard Material

Abstract. Background: The public health sector has advocated for more innovative, technology-based, suicide prevention education for the community, to improve their ability to detect and respond to suicide risk. Emerging evidence suggests addressing the bystander effect through the Bystander Intervention Model (BIM) in education material may have potential for suicide prevention. Aims: The current study aimed to assess whether BIM-informed tools can lead to improved readiness, confidence and intent in the community to detect and respond to suicide risk in others. Method: A sample of 281 adults recruited from the community participated in a randomized controlled trial comprising a factsheet designed according to the BIM (intervention group) and a standard factsheet about suicide and mental health (control group). Participants' self-reported detecting and responding to suicide risk readiness, confidence, and intent when presented with a suicidal peer was tested pre- and postintervention and compared across time and between groups. Results: The intervention group had significantly higher levels of detecting and responding to suicide risk readiness, confidence, and intent than the control group at postintervention (all p < .001) with moderate-to-large effect sizes. Limitations: The study was limited by a homogenous sample, too low numbers at follow-up to report, and self-report data only. Conclusion: This study demonstrates BIM-informed suicide prevention training may enhance the community's intervention readiness, confidence, and intent better than current standard material. Further testing in this area is recommended. While results were statistically significant, clinical significance requires further exploration.

Prekindergarten Screener: Implementing the Response-to-Intervention Model in Ecuador

2009 ◽  
Author(s):  
Arlene Ortiz ◽  
Mauela Guimarães ◽  
Tara C. Raines ◽  
Patricio A. Romero
Keyword(s):  
Response To Intervention ◽  
Intervention Model ◽  
Response To Intervention Model

Construct Validity

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Construct Validity ◽  
Hypothesis Test ◽  
Secondary Data ◽  
Time Frame ◽  
Primary Data ◽  
Optimal Time ◽  
Intervention Model ◽  
Right Hand ◽  
The Right

Chapter 8 focuses on threats to construct validity arising from the left-hand side time series and the right-hand side intervention model. Construct validity is limited to questions of whether an observed effect can be generalized to alternative cause and effect measures. The “talking out” self-injurious behavior time series, shown in Chapter 5, are examples of primary data. Researchers often have no choice but to use secondary data that were collected by third parties for purposes unrelated to any hypothesis test. Even in those less-than-ideal instances, however, an optimal time series can be constructed by limiting the time frame and otherwise paying attention to regime changes. Threats to construct validity that arise from the right-hand side intervention model, such as fuzzy or unclear onset and responses, are controlled by paying close attention to the underlying theory. Even a minimal theory should specify the onset and duration of an impact.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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