MétaCan
Menu
Back to cohort
Record W4403605956 · doi:10.3390/healthcare12202089

Urban–Rural Disparities in Community Participation after Spinal Cord Injury in Ontario

2024· article· en· W4403605956 on OpenAlexaffabout
Mohammadreza Amiri, Mohammad Alavinia, Farnoosh Farahani, Natavan Khasiyeva, Meredith Burley, Suban Kangatharan, B. Catharine Craven

Bibliographic record

VenueHealthcare · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of TorontoSpinal Cord Injury OntarioUniversity Health Network
Fundersnot available
KeywordsRuralityDescriptive statisticsMedicineGerontologySpinal cord injuryRural areaActivities of daily livingLogistic regressionEnvironmental healthGeographyPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Personal, social, and environmental factors may influence self-efficacy and social reintegration among people living with spinal cord injury or disease (SCI/D) in urban and rural areas. Novel data collection methods have the potential to characterize community participation (CP) in diverse settings. OBJECTIVES: The objectives were (1) to describe and compare self-reported community participation (Reintegration to Normal Living Index (RNLI) and Moorong Self-Efficacy Scale (MSES)) levels of individuals with SCI/D living in urban or rural Ontario, Canada; and (2) to determine the accuracy of an artificial intelligence (AI) optical mark recognition tool for extracting data from CP surveys conducted among participants after transitioning from inpatient rehabilitation to home and residing in the community. METHODS: We partnered with SCI Ontario staff to collect MSES and RNLI survey data from adults with motor complete (e.g., AIS A-B) and incomplete (AIS C-D) SCI/D living in urban or rural Ontario, Canada, between January and October 2022. The Rurality Index of Ontario (RIO) from the postal code determined urban or rural residency. Optical mark recognition (OMR) software was used for survey data extraction. A Research Associate validated the extracted survey responses. Descriptive statistics, correlation analysis, and non-parametric statistics were used to describe the participants, their impairments, and their reported CP levels across urban and rural settings. RESULTS: Eighty-five individuals with SCI/D (mean age 53.7 years, 36.5% female) completed the survey. Most of the participants resided in major urban areas (69.4%) and had traumatic injuries (64.7%). The mean total MSES score for Ontarians with SCI/D was 87.96 (95% confidence interval [CI]: 84.45, 91.47), while the mean total RNLI score for the same individuals was 75.61 (95% CI: 71.85, 79.37). Among the MSES domains, the lowest score was observed in response to sexual satisfaction (mean: 4.012, 95% CI: 3.527, 4.497), while the lowest RNLI domain item score was associated with the ability to travel out of town (mean: 5.965, 95% CI: 5.252, 6.678). Individuals with incomplete injuries in rural areas reported lower MSES and RNLI scores than those with complete motor injuries, whereas no significant differences were found in MSES and RNLI scores among urban residents based on impairment. These findings suggest that, depending on the environmental context (e.g., rural vs. urban areas), AIS categories may influence the perception of CP among people living with SCI/D. The OMR tool had 97.4% accuracy in extracting data from the surveys. CONCLUSIONS: The CP (MSES and RNLI) scores reported by individuals with SCI/D differ based on their living setting. In rural Ontario, individuals with greater functional ability reported lower CP than their counterparts living in urban settings. Although CP remains a challenge, the needs of individuals with motor incomplete SCI/D and heterogeneous levels of mobility residing in rural areas require exploration and targeted interventions. The OMR tool facilitates accurate data extraction from surveys across settings.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.120
GPT teacher head0.461
Teacher spread0.341 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueHealthcareSame topicSpinal Cord Injury ResearchFrench-language works237,207