A Cluster Analysis of Peer Support Training Needs for Foster Parents

This paper describes the use of cluster analysis to aid in empirically validating the course objectives of an industrial training curriculum. For instance, clusters of basic training needs were found and compared against existing course contents. Two major findings emerged from the data analysis: the discovery of an important job activity missing from the curriculum and the identification of sizeable groups of workers with distinctly different training needs. Considerable attention is also devoted to the statistical aspects of clustering which were important in obtaining these results.

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Engagement tactics with foster parents: Experiences of resource workers

Social Work and Social Sciences Review ◽

10.1921/swssr.v18i3.956 ◽

2016 ◽

Vol 18 (3) ◽

pp. 80-100

Author(s):

Jason D Brown ◽

Sarah Serbinski ◽

Julie Gerritts ◽

Landy Anderson

Keyword(s):

Cluster Analysis ◽

Child Welfare ◽

Multidimensional Scaling ◽

Metropolitan Area ◽

Foster Parents ◽

Child Welfare Workers ◽

Large Metropolitan Area ◽

And Cluster Analysis ◽

Welfare Workers ◽

Parents Experiences

Resource workers are child welfare workers who work closely with foster parents following placement of a child placed in care. One of the challenges they experience is reluctance to their involvement. Resource workers from a large metropolitan area were asked: “What do you do when foster parents are reluctant about your involvement?” Responses to this question were analyzed with multidimensional scaling and cluster analysis. Nine concepts resulted, including: Recognize Problems, Build Trust, Go the Extra, Be Positive, Broaden their Network, Find Commonalities, Set Limits, Understand Them, and Reinforce Accountability. These concepts were compared and contrasted with the available literature.

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Differentiating Experience From Cue Utilization in Radiological Assessments

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/0018720820902576 ◽

2020 ◽

pp. 001872082090257 ◽

Cited By ~ 2

Author(s):

Ann J. Carrigan ◽

John Magnussen ◽

Andrew Georgiou ◽

Kim M. Curby ◽

Thomas J. Palmeri ◽

...

Keyword(s):

Cluster Analysis ◽

Diagnostic Accuracy ◽

Diagnostic Performance ◽

Assessment Tool ◽

Image Interpretation ◽

Training Needs ◽

Years Of Experience ◽

Cue Utilization ◽

Domain Specific

Objective This research was designed to examine the contribution of self-reported experience and cue utilization to diagnostic accuracy in the context of radiology. Background Within radiology, it is unclear how task-related experience contributes to the acquisition of associations between features with events in memory, or cues, and how they contribute to diagnostic performance. Method Data were collected from 18 trainees and 41 radiologists. The participants completed a radiology edition of the established cue utilization assessment tool EXPERTise 2.0, which provides a measure of cue utilization based on performance on a number of domain-specific tasks. The participants also completed a separate image interpretation task as an independent measure of diagnostic performance. Results Consistent with previous research, a k-means cluster analysis using the data from EXPERTise 2.0 delineated two groups, the pattern of centroids of which reflected higher and lower cue utilization. Controlling for years of experience, participants with higher cue utilization were more accurate on the image interpretation task compared to participants who demonstrated relatively lower cue utilization ( p = .01). Conclusion This study provides support for the role of cue utilization in assessments of radiology images among qualified radiologists. Importantly, it also demonstrates that cue utilization and self-reported years of experience as a radiologist make independent contributions to performance on the radiological diagnostic task. Application Task-related experience, including training, needs to be structured to ensure that learners have the opportunity to acquire feature–event relationships and internalize these associations in the form of cues in memory.

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The Role of Peer Support, Superior Support, Training Self-Efficacy, Training Needs and Opportunity to Use on Readiness to Participate in Training Among Civil Servants in Nigeria

International Journal of Academic Research in Business and Social Sciences ◽

10.6007/ijarbss/v10-i12/8142 ◽

2020 ◽

Vol 10 (12) ◽

Author(s):

Nnamdi Ogbodoakum ◽

Ahmad Fauzi Mohd Ayub ◽

Norhasni Zainal Abiddin

Keyword(s):

Peer Support ◽

Self Efficacy ◽

Training Needs ◽

Civil Servants

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Nebraska Supreme Court Rules a Minor in Foster Care Seeking an Abortion Must Obtain Foster Parents Consent – In re Anonymous 5

American Journal of Law & Medicine ◽

10.1017/s0098858800000083 ◽

2014 ◽

Vol 40 (1) ◽

pp. 172-174

Author(s):

Tulsi A. Patel

Keyword(s):

Foster Care ◽

Supreme Court ◽

Foster Parents ◽

Care Seeking ◽

A Minor

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Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100132078 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1488-1489

Author(s):

Thomas W. Shattuck ◽

James R. Anderson ◽

Neil W. Tindale ◽

Peter R. Buseck

Keyword(s):

Cluster Analysis ◽

Chemical Reactivity ◽

Large Data ◽

Large Data Sets ◽

Particle Analysis ◽

Data Sets ◽

Halogen Chemistry ◽

Complete Study ◽

Components Analysis ◽

Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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Measuring “Language Access Profiles” in Deaf and Hard-of-Hearing Children With the DHH Language Exposure Assessment Tool

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00439 ◽

2020 ◽

pp. 1-25

Author(s):

Matthew L. Hall ◽

Stephanie De Anda

Keyword(s):

Cluster Analysis ◽

Exposure Assessment ◽

Hard Of Hearing ◽

Assessment Tool ◽

Communication Mode ◽

Developmental Potential ◽

Language Access ◽

Retest Reliability ◽

Language Exposure ◽

Test Retest Reliability

Purpose The purposes of this study were (a) to introduce “language access profiles” as a viable alternative construct to “communication mode” for describing experience with language input during early childhood for deaf and hard-of-hearing (DHH) children; (b) to describe the development of a new tool for measuring DHH children's language access profiles during infancy and toddlerhood; and (c) to evaluate the novelty, reliability, and validity of this tool. Method We adapted an existing retrospective parent report measure of early language experience (the Language Exposure Assessment Tool) to make it suitable for use with DHH populations. We administered the adapted instrument (DHH Language Exposure Assessment Tool [D-LEAT]) to the caregivers of 105 DHH children aged 12 years and younger. To measure convergent validity, we also administered another novel instrument: the Language Access Profile Tool. To measure test–retest reliability, half of the participants were interviewed again after 1 month. We identified groups of children with similar language access profiles by using hierarchical cluster analysis. Results The D-LEAT revealed DHH children's diverse experiences with access to language during infancy and toddlerhood. Cluster analysis groupings were markedly different from those derived from more traditional grouping rules (e.g., communication modes). Test–retest reliability was good, especially for the same-interviewer condition. Content, convergent, and face validity were strong. Conclusions To optimize DHH children's developmental potential, stakeholders who work at the individual and population levels would benefit from replacing communication mode with language access profiles. The D-LEAT is the first tool that aims to measure this novel construct. Despite limitations that future work aims to address, the present results demonstrate that the D-LEAT represents progress over the status quo.

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