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Record W7139221276

Understanding the Housing Experiences of Individuals with Spinal Cord Injury/Dysfunction

2025· dissertation· W7139221276 on OpenAlexaboutno aff
Sarmitha Sivakumaran

Bibliographic record

VenueTSpace (University of Toronto) · 2025
Typedissertation
Language
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsQualitative researchPublic housingTheme (computing)Qualitative analysisQualitative propertyHousing First
DOInot available

Abstract

fetched live from OpenAlex

Many individuals identify housing as a significant concern following a spinal cord injury/dysfunction (SCI/D). This thesis comprises a scoping review and a qualitative study, both aimed at enriching the evidence examining housing experiences for individuals with SCI/D. The scoping review identified 36 eligible studies, highlighting the housing challenges experienced by the SCI/D community. The qualitative study explored the housing experiences of individuals with SCI/D in Ontario, Canada (N=21). One over-arching theme of ‘Feeling Lucky’ as well as three main themes were identified from the qualitative data: 1) In/adequate housing “(didn’t really) meet my needs”: varying levels of housing satisfaction; 2) “Live with it”: limited choice of housing options for the SCI/D community; and 3) “Home sweet home”: strategies to enhance home comfort. The thesis advances evidence on SCI/D and housing, which may serve to inform practice and policy for this relatively understudied yet impactful issue for the SCI/D community.

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)0.005
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.095
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.362
Teacher spread0.269 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)→Same topicSpinal Cord Injury Research→French-language works237,207→