“Forget about the glass ceiling, I’m stuck in a glass box”: A meta-ethnography of work participation for persons with physical disabilities
Bibliographic record
Abstract
BACKGROUND: Finding and sustaining employment can be a challenge for persons with a physical disability (PwPD) because they may be limited in the work they can do, may require workplace accommodations, or experience discrimination. OBJECTIVE: Our aim was to understand how successfully employed PwPDs find and sustain employment, and to use this information to build a conceptual model. METHODS: We searched published studies on physical disability and employment from electronic databases (1980–2015) and bibliographical reviews of retrieved studies. We used meta-ethnography to synthesize the findings. RESULTS: We reviewed 19 studies and identified 10 themes highlighting common issues experienced by PwPDs. Using these themes, we developed a process model to illustrate the dynamic employment process PwPDs’ experience and the factors that create barriers or facilitators as they attempt to find, maintain employment, and/or advance at work. CONCLUSIONS: PwPDs encounter a range of barriers at different stages of their employment journey which make them feel “stuck” and “exposed” in lower-level positions with little opportunity to advance or to move laterally within an organization. This study provides a framework to help rehabilitation specialists, employers, and researchers understand what PwPDs need at each stage of their employment journey to attain more sustainable employment outcomes.
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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 | 0.033 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".