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Record W7132960080

Impact of Traumatic Spinal Cord Injury on Income and Employment Status in a National Canadian Cohort

2023· dissertation· W7132960080 on OpenAlexaffabout
Rachael Jaffe

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsInstitute of Health Services and Policy Research
Fundersnot available
KeywordsEarningsCohortSpinal cord injuryAffect (linguistics)Retrospective cohort studyCohort studyOccupational safety and healthRehabilitation
DOInot available

Abstract

fetched live from OpenAlex

Background: Spinal Cord Injury (SCI) causes drastic changes to an individual’s health that can affect returning to work. Objective: To use national administrative health and tax databases to estimate the impact of SCI on income and employment. Study Design: A retrospective cohort study of adults who were hospitalized with SCI in Canada between January 2005 and December 2017. Main outcomes: The first outcome was the change in individual annual earnings up to 5 years postinjury. The second outcome was the changes in employment status up to 5 years postinjury. Results: The mean annual decline in earnings in the 5 years postinjury was -$21,435 (95% CI, −$26,028 to −$16,841). Five years postinjury 50% of injured individuals were employed, a decrease of -18 (95% CI, -22, to -17) percentage points. Conclusions: SCI had a significant association with a decrease in employment and earnings postinjury for adults aged 18 to 64 in Canada.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.073
GPT teacher head0.487
Teacher spread0.414 · 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
Published2023
Admission routes2
Has abstractyes

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