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Record W4395012055 · doi:10.5206/ijoh.2023.3.16452

Comparison of Transportation Use Among Older and Younger Persons Experiencing Homelessness

2024· article· en· W4395012055 on OpenAlexvenueno aff
Sarah L. Canham, Olivia Huntzinger, Jeff Rose, Shannon Jones, Ivis García

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyGerontologyMedicine

Abstract

fetched live from OpenAlex

Regardless of age, transportation determines access to necessary services and supports, including food, healthcare, shelter, and social connection for persons experiencing homelessness (PEH). This study compared experiences and perceptions of transportation among PEH aged <49 years old to PEH aged 50+ years. We conducted a secondary qualitative data analysis of semi-structured interviews with 18 PEH staying in emergency shelters in Salt Lake County, Utah. Using thematic analysis, we identified three points of comparison between older and younger PEH: 1) Modes of transportation used; 2) Reasons for using public transit; and 3) Perceptions of public transit. While there are some similarities across transportation modes used (e.g., public transit, dedicated homeless-services shuttle, walking), older PEH also reported using taxi services when it could be afforded or shared with others. In addition, older PEH more often discussed using public transit to get to healthcare appointments, while younger PEH reported needing transit to get to places of employment. Though both participant groups described the prohibitive costs of transit, OPEH recommended that to use transit most successfully there need to be places to sit and rest when walking, as well as more publicly available bathrooms and crosswalks for safety. This age-based comparison offers insight into ways to increase transportation equity and support PEH of all ages who similarly have unmet mobility needs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.063
GPT teacher head0.434
Teacher spread0.371 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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