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Record W1965176578 · doi:10.1007/s10464-015-9714-2

Perceptions of Private Market Landlords Who Rent to Tenants of a Housing First Program

2015· article· en· W1965176578 on OpenAlexafffundabout
Tim Aubry, Rebecca Cherner, John Ecker, Jonathan Jetté, Jennifer Rae, Stéphanie Yamin, John Sylvestre, Jimmy Bourque, Nancy McWilliams

Bibliographic record

VenueAmerican Journal of Community Psychology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMoncton HospitalUniversité de MonctonUniversity of Ottawa
FundersHealth CanadaMental Health Commission
KeywordsRentingContext (archaeology)Public housingBusinessPerceptionRental housingQualitative researchFinanceEconomic growthEconomicsSociologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The rental of housing units by landlords to participants in Housing First (HF) programs is critical to the success of these programs. Therefore, it is important to understand the experiences of landlords with having these individuals as tenants. The paper presents findings of qualitative interviews with 23 landlords who rented to tenants from a HF program located in a small city and adjoining rural area in eastern Canada and in which approximately 75 % of tenants had been housed for at least six consecutive months at 2 years in the program. Findings showed that landlords are motivated to rent to HF tenants for financial and pro-social reasons. They reported holding a range of positive, neutral, and negative perceptions of these tenants. They identified problems encountered with some HF tenants that included disruptive visitors, conflict with other tenants, constant presence in their apartments, and poor upkeep of units. On the other hand, landlords perceived HF tenants as being mostly good tenants who are similar to their other tenants. Implications for practice in the context of HF programs are discussed.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.111
GPT teacher head0.506
Teacher spread0.395 · 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 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

Citations15
Published2015
Admission routes3
Has abstractyes

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