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‘An accessible route is always the longest’: older adults’ experience of their urban environment captured by user-led audits and photovoice

2021· book-chapter· en· W3152321066 on OpenAlexaboutno aff
Atiya Mahmood, Delphine Labbé

Bibliographic record

VenuePolicy Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoiceNeighbourhood (mathematics)AuditCompetence (human resources)Built environmentAging in placeOlder peoplePsychologyGeographyGerontologySocial psychologyBusinessEngineeringMedicineEconomic growthCivil engineering

Abstract

fetched live from OpenAlex

This chapter presents the results of neighbourhood built-environment audits and photo elicitation from a study conducted in the Greater Vancouver Area in British Columbia (BC) in order to explore the barriers and facilitators encountered by older mobility device (MD) users. It analyzes propositions of the ecological model of aging, which explains how the environment plays a significant role in outcomes for older persons experiencing a decline in competence. It also discusses immediate home and neighbourhood environments that are important for older adults, as they are less likely to be working or have the ability to access a variety of locations in the urban environment. The chapter elaborates how very old people tend to decrease their action range and spend large portions of the day at home and the 'immediate outdoor environment'. It reviews research that shows that the physical environment close to home has a strong relationship with mobility and social participation among older adults.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2021
Admission routes1
Has abstractyes

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