MétaCan
Menu
Back to cohort
Record W4308956750 · doi:10.1002/ajcp.12634

“How does your residential environment positively or negatively influence your well‐being?”: A multicase photovoice study with public housing tenants

2022· article· en· W4308956750 on OpenAlexaffabout
Stéphanie Radziszewski, Janie Houle, Juan Torres, Xavier Leloup, Simon Coulombe

Bibliographic record

VenueAmerican Journal of Community Psychology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversité LavalInstitut National de la Recherche ScientifiqueUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsPhotovoicePublic housingBuilt environmentSociologyEmpowermentBusinessPublic relationsEconomic growthPolitical scienceEngineeringCivil engineeringEconomics

Abstract

fetched live from OpenAlex

Public housing aims to reduce social inequalities by providing affordable dwellings as a social policy. Anchored in an ecological perspective, the paper reports on a multicase photovoice study documenting public housing tenants' perceptions of how their residential environment influences their well-being. This design can provide a deeper understanding of the public housing environment to inform change at a programmatic level. To this end, 303 captioned photos were collected by 59 tenant-researchers at six sites in Québec (Canada). An in-depth cross-case analysis of the material led to two key themes with five subthemes each. In the Residential environment perceived as mostly positive theme, the subthemes were access to nature, community resources and services, positive relations among tenants, opportunities for participation, and specific aspects of their home. In the Negative aspects focused on life in public housing theme, the subthemes were strict regulations, lack of respect for tenants' needs, lack of intimacy, lack of proper maintenance, and conflicts between tenants. Findings highlight the dynamic interplay between the residential environment and public housing tenants' well-being. Two recurring programmatic issues are highlighted: problematic maintenance and limited opportunities for tenants' empowerment. Changes to address these concerns at the programmatic level of public housing could potentially increase tenants' well-being.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.325
Teacher spread0.285 · 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 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

Citations8
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Community PsychologySame topicUrban Green Space and HealthFrench-language works237,207