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Record W4405067500 · doi:10.3390/youth4040108

Towards the Prevention of Youth Homelessness

2024· article· en· W4405067500 on OpenAlexafffundabout
Stephen Gaetz, Amanda Buchnea, Cathy Fournier, Erin Dej, Kaitlin Schwan

Bibliographic record

VenueYouth · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsWilfrid Laurier UniversityYork University
FundersGovernment of Canada
KeywordsTypologyPsychological interventionCLARITYIntervention (counseling)Set (abstract data type)Political sciencePublic relationsSociologyPsychologyMedicineNursingComputer science

Abstract

fetched live from OpenAlex

Historically, the prevention of youth homelessness has not been a priority in Canada or the United States. In recent years, this has begun to change. While there is growing recognition that a shift by preventing homelessness is required to bring a substantive end to homelessness, a common and shared understanding of what prevention is and what it involves has remained largely absent or obscured in both policy and practice. In this paper, we focus specifically on the prevention of youth homelessness and set out to provide conceptual clarity through presenting a clear definition of what prevention is and what it is not. Accompanying the definition is a five-point typology that includes (1) structural prevention; (2) systems prevention; (3) early intervention; (4) crisis intervention, and (5) housing stabilization. Each of the five elements of the typology is defined, identifying who is responsible for implementation. In addition, the typology is populated with examples of different approaches to the prevention of youth homelessness. We conclude with some key considerations to guide the implementation of preventive interventions and present core principles designed to support the development of effective and quality prevention interventions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.102
GPT teacher head0.410
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2024
Admission routes3
Has abstractyes

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