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Thinking about environment: incorporating geographies of disability into rehabilitation science

2003· article· en· W2077020564 on OpenAlexaffvenue
Isabel Dyck, Patti O’Brien

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Identity, and Health
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsNeighbourhood (mathematics)Space (punctuation)Meaning (existential)Occupational therapySociologyRehabilitationSociology of health and illnessHealth carePsychologyEpistemologyPsychotherapistComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

This paper concerns the introduction of geographical perspectives and concepts to health professionals in their analysis of disability or chronic illness. It focuses specifically on a course project, which drew on geographical literature and concepts from social theory in ‘mapping’ the daily routines of people with disability or chronic illness. It presents an analysis of the daily routines of a man with HIV/AIDS, showing the close and recursive interweaving of meanings of space and bodily inscription as a man under palliative care negotiates his body, neighbourhood and medical care. It describes his changing relationship to the spaces constituting his everyday life, and their renewed meaning as medical care becomes a more prominent theme in how such spaces are used. The relevance of this mapping of the chronically ill self into place and space to health professions is discussed through the particular lens of occupational therapy, which seeks to understand theoretically the inter‐linking of client behaviour and ‘environment’ and its implications for clinical reasoning.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.209
Threshold uncertainty score0.415

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0060.058
Scholarly communication0.0100.010
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 designTheoretical or conceptual
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

Citations14
Published2003
Admission routes2
Has abstractyes

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Same venueCanadian Geographies / Géographies canadiennesSame topicMigration, Identity, and HealthFrench-language works237,207