MétaCan
Menu
Back to cohort
Record W2944886952

Best Practices for Maintaining Housing with Intellectually Disabled John Howard Clients

2017· article· en· W2944886952 on OpenAlexaffabout
Laura Quinlan

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsMacEwan University
Fundersnot available
KeywordsIndependence (probability theory)Service (business)Supportive housingPopulationIntellectual disabilityPsychologyHousing FirstPublic relationsPresentation (obstetrics)SociologyGerontologyMental healthBusinessMedicinePsychiatryPolitical scienceMarketingMental illness
DOInot available

Abstract

fetched live from OpenAlex

Intellectually disabled offenders are a heterogenous group with varying needs and abilities. Therefore, further study is required to meet the needs of this diverse population that is overrepresented in the criminal justice system. The objective of this research is to create a sense of understanding in regards to housing and support services needed for criminalized or high needs individuals with intellectual disabilities who are housed at Independence Apartments, a federal halfway house run by the Edmonton John Howard Society. Semi-structured interviews were conducted with stakeholders of the Persons with Developmental Disabilities (PDD) program at Independence Apartments. These stakeholders included professionals, PDD clients, and non PDD clients also housed at Independence Apartments. This presentation examines the effectiveness of having both PDD and non PDD programs running under one roof and explores the support service needs of the PDD clients housed at Independence Apartments. Preliminary results show that moving the PDD program to a separate facility would be beneficial for the PDD client group because the PDD clients tend to have difficulty following house rules and are often taken advantage of by the clients in the halfway house program. Discipline: Sociology Faculty Mentor: Dr. Michael Gulayets

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.560
GPT teacher head0.601
Teacher spread0.041 · 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 teacher head, not a consensus.

Study designQualitative
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueStudent Research ProceedingsSame topicHealthcare innovation and challengesFrench-language works237,207