Predictors for work participation of people with visual impairments: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Introduction The aim of this systematic review and meta‐analysis was to assess factors associated with work participation in people with visual impairments and to explore how these factors may have changed over time. Method A comprehensive search of PubMed, Embase.com , EBSCO/APA PsycInfo, EBSCO/CINAHL and EBSCO/ERIC from database inception to 1 April 2022 was performed. We included studies with cross‐sectional design, case–control, case‐series or cohort design, involving visually impaired working‐age adults with at least moderate visual impairment, and evaluated the association between visual impairment and work participation. Studies involving participants with deaf‐blindness or multiple disabilities were excluded. We assessed study quality (Newcastle–Ottawa Scale [NOS]), examined between‐study heterogeneity and performed subgroup analyses. The study protocol was registered in PROSPERO, CRD42021241076. Results Of 13,585 records, 57 articles described 55 studies including 1,326,091 participants from mostly high‐income countries. Sociodemographic factors associated with employment included higher education (odds ratio [OR] 3.34, 95% confidence interval [CI] 2.47 to 4.51, I 2 0%), being male (OR 1.59, 95% CI 1.37 to 1.84, I 2 95%), having a partner (OR 1.73, 95% CI 1.12 to 2.67, I 2 34%), white ethnicity (OR 1.36, 95% CI 1.07 to 1.74, I 2 0%) and having financial assistance (OR 0.38, 95% CI 0.26 to 0.55, I 2 85%). Disease‐related factors included worse visual impairment (OR 0.61, 95% CI 0.46 to 0.80, I 2 98%) or having additional disabilities (OR 0.55, 95% CI 0.49 to 0.62, I 2 16%). Intervention‐related factors included mobility aid utilisation (OR 0.35, 95% CI 0.10 to 1.18, I 2 94%). A potential moderating effect of time period and geographical region was observed for some factors. Study quality (NOS) was rated moderate to high. Conclusion Several sociodemographic and disease related factors were associated with employment status. However, the results should be interpreted with caution because of overall high heterogeneity. Future research should focus on the role of workplace factors, technological adjustments and vocational rehabilitation services on work participation.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".