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Record W150102863

Changing through Clusters: Vermont's Policy Clusters

2003· article· en· W150102863 on OpenAlexaboutno aff
Will Lindner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationCommissionLegislatureState (computer science)CriminologyPsychiatryLawPolitical sciencePsychologySociologyDemography
DOInot available

Abstract

fetched live from OpenAlex

Let's acknowledge at the outset that Gloria is fictional. But there are real-life Glorias even in Vermont, a state known for its picturesque villages, ski slopes, and green pastures. They are abuse victims whose husbands or boyfriends are serving time in jail. In this scenario, says Dr. Thomas Powell, director of clinical services with the Vermont Department of Corrections, let's place Gloria's husband at the Northern State Correctional Facility, one of Vermont's highest-security prisons, within hailing distance of the Canadian border in Vermont's rugged Northeast Kingdom. Traveling to see him on visitors' day is an arduous proposition. So what do we do when she gets there, with her children in tow? asks Powell. We greet her with security apparatus and security officials who are large males, and who make her go through metal detectors and contraband-detection procedures. If this is a woman with a history of traumatic abuse, she feels threatened, intimidated, and controlled from the moment she enters our facility. The same holds for her children, who may have been abused, or have witnessed abuse, themselves. But the Glorias and their children aren't the only ones affected by trauma in Vermont. Trauma appears across the spectrum of the human services client population. A legislative Commission on Psychological Trauma reported in 2000 that perhaps 70 percent of people in Vermont's outpatient mental health treatment programs, and up to 72 percent in inpatient facilities, had traumatic abuse histories. Seventy percent of the participants in substance abuse treatment programs experienced physical or sexual abuse. The trend holds even for PATH (the Department of Prevention, Assistance, Transition and Health Access, formerly Social Welfare). one primary reason they end up in (need of) our agency's says Susan Besio, commissioner of Vermont's Developmental and Mental Health Services. To address the high incidence of trauma, in April 2001, Jane Kitchel, Vermont's then secretary of human services, designated trauma as the Agency of Human Services' (AHS) seventh, and most recent, cluster. Kitchel appointed Besio cochair of a cross-departmental working group. In September 2002 the cluster hosted a three-day, agency-wide training with nationally recognized trauma experts Maxine Harris and Roger D. Fallot. The objective was to make the disparate departments operating under AHS' wide umbrella trauma-informed, modifying procedures to lessen the chances of alienating people with trauma histories, and to become more knowledgeable about referrals for treatment. It's an important step because long-term trauma, with its pernicious emotional consequences, can subvert the best efforts of human services providers. Once we start screening effectively for trauma throughout the agency, it might be the cross-cutting issue that allows us an understanding of families' systems and provides new perspectives on how to treat them, says Powell. Agency administrators have similar ambitions for all seven policy clusters (though the cluster concept itself is fluid, so more clusters could be created while existing clusters tail off as their work is completed or morph into some variation on their original theme.) Besides Trauma, there are clusters for Integrated Service Delivery, the High-Risk Pool, Coordinating the Caregiver Process, Connecting Better with Communities, Crisis and Family Stabilization, and a Coordinated Approach to Home Visits. Adaptable and Fluid The goal of transforming not just a department or two, but the entire agency; into a trauma-informed social services organization suggests the potential dimensions of Vermont's policy cluster concept. If AHS tried to address trauma in the pre-policy cluster mode, one might almost imagine the creation of a Department of Trauma, replete with bureaucracy, budget, and reporting requirements. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.548
Threshold uncertainty score0.910

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0480.014
Scholarly communication0.0270.023
Open science0.0090.038
Research integrity0.0330.023
Insufficient payload (model declined to judge)0.0450.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.206
GPT teacher head0.494
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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