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Record W4413640897 · doi:10.5206/ijoh.2023.3.21839

Cognition and Social Cognition in People Experiencing Homelessness

2025· article· en· W4413640897 on OpenAlexvenueno aff
Hannah Reene, Matthew H. Jones‐Chesters

Bibliographic record

VenueInternational Journal on Homelessness · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionPsychologySocial cognitionCognitive psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Objective: To understand the social cognitive profile of a population of people experiencing homelessness. To identify to what extent social cognition and other cognitive functions were related in the present sample. Methods: A cross-sectional design was employed to recruit thirty-five English speaking participants, who completed a battery of tests of cognition and social cognition. Social cognition measures included the Affect Naming Task, Mentalisation Stories, and the Questionnaire of Cognitive and Affective Empathy. Results: Participant performance on objective measures of social cognition (Affect Naming, Mentalisation Stories) were poorer than expected based on normative data. A number of weaknesses on cognitive tasks assessing verbal learning and memory and attentional-executive functions were identified. Cognitive functions in other domains correlated with but was not predictive of social cognition performance. Conclusions: This is the first study to identify weaknesses in social cognition in a sample of people experiencing homelessness without veteran status. People who are homeless should be routinely offered assessment of their cognition and social cognition, in order to better support their health and housing needs. Future research with more diverse samples and longitudinal follow-up will strengthen our understanding of social cognition in people who are homeless.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.407
Teacher spread0.373 · 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
Published2025
Admission routes1
Has abstractyes

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