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Record W4401538340 · doi:10.1016/j.jenvp.2024.102407

The influence of the residential environment on well-being and personal projects: Perspectives of young people living in public housing

2024· article· en· W4401538340 on OpenAlexafffundabout
J. Latreille, Janie Houle, Simon Coulombe

Bibliographic record

VenueJournal of Environmental Psychology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité LavalCentre for Social InnovationInstitut universitaire en santé mentale de MontréalInstitut Universitaire en Santé Mentale de QuébecUniversité du Québec à Montréal
FundersFonds de Recherche du Québec-Société et Culture
KeywordsPsychologySociology

Abstract

fetched live from OpenAlex

Public housing provides low-income households with affordable housing. The few studies of young public housing residents have focused on the negative aspects of these residential environments, such as the presence of economic hardship or substance abuse. Few studies have looked at the possible positive influence of public housing on the lives of the young people who live there. This qualitative study explores the influence of housing and neighborhood on the well-being and personal projects of young people living in public housing. Semi-structured interviews were conducted with 30 young people aged 14 to 20 living in public housing in a large city in Quebec, Canada. A thematic analysis led to the identification of five themes that describe the components of the residential environment that influence young people's well-being and personal projects: 1) parks, playgrounds and nature, 2) services and activities, 3) privacy, 4) relations with the neighborhood; and 5) the quality of the built environment. Implications for environmental psychologists and for improving public policies and supporting the well-being of young people living in public housing 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.280

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 integrity0.0000.001
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 head0.021
GPT teacher head0.357
Teacher spread0.335 · 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

Citations9
Published2024
Admission routes3
Has abstractyes

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