Modifiable and non-modifiable factors associated with employment outcomes following spinal cord injury: A systematic review
Bibliographic record
Abstract
CONTEXT: Employment rates in individuals with spinal cord injury (SCI) are approximately 35%, which is considerably lower than that of the general population. In order to improve employment outcomes a clear understanding of what factors influence employment outcomes is needed. OBJECTIVE: To systematically review factors that are consistently and independently associated with employment outcomes in individuals with SCI, and to understand the magnitude of their influence. METHODS: Through an electronic search of MEDLINE/PubMed, EMBASE, CINAHL, PsycINFO, Social Science Abstracts and Social Work databases, we identified studies published between 1952-2014 that investigated factors associated with employment outcomes following SCI. Exclusion criteria included: (1) reviews (2) studies not published in English (3) studies not controlling for potential confounders through a regression analysis, or (4) studies not providing an effect measure in the form of OR, RR, or HR. Data were categorized based on the International Classification of Functioning, Disability and Health framework, with each domain sub-categorized by modifiability. First author, year of publication, sample size, explanatory and outcome variables, and effect measures were extracted. RESULTS: Thirty-nine studies met the inclusion criteria. Twenty modifiable and twelve non-modifiable factors have been investigated in the context of employment following SCI. Education, vocational rehabilitation, functional independence, social support, and financial disincentives were modifiable factors that have been consistently and independently associated with employment outcomes. CONCLUSION: A number of key modifiable factors have been identified and can inform interventions aimed at improving employment outcomes for individuals with SCI. Future research should focus on determining which factors have the greatest effect on employment outcomes, in addition to developing and evaluating interventions targeted at these factors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".