A Multidisciplinary Model to Guide Employment Outcomes Among People Living With Spinal Cord Injuries in South Africa: A Mixed Methods Study Protocol
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) often results in complete or partial loss of functioning of the upper and/or lower limbs, leading to the affected individual experiencing difficulties in performing activities of daily living. This results in reduced participation in social, religious, recreational, and economic activities (employment). The South Africa legal framework promotes the employment and assistance of people with disabilities. However, rehabilitation interventions focus mainly on impairments and activity limitations, with few attempts to prepare those with SCI to return to gainful employment. There is therefore a need for a well-coordinated, multidisciplinary rehabilitation initiative that will promote the employment of people living with spinal cord injuries (PLWSCI) in South Africa. OBJECTIVE: This study aims to develop a multidisciplinary model to guide employment outcomes amongst PLWSCI in South Africa. METHODS: This study will utilize explanatory mixed methods during 3 phases. The first phase will explore the current rehabilitation practices, and the second will establish the factors that influence employment outcomes among PLWSCI. A multidisciplinary team consisting of health care professionals, representatives from the departments of Labour, Education, Social Development, and Health, and nongovernment organizations representing PLWSCI will provide feedback for the model development of phase 3, along with results from the previous 2 phases, using a multistage Delphi technique. RESULTS: It is estimated that the results of phases 1 and 2 will be completed 11 months after data collection commencement (November 2015). Phase 3 results will be finalized 4 months after phases 1 and 2. CONCLUSIONS: Developing a multidisciplinary model to guide the employment outcomes of PLWSCI will ensure a coordinated response to integrate them into a productive life and will assist them to achieve economic self-sufficiency, personal growth, social integration, life satisfaction, and an improved quality of life. This can be achieved by active inclusion of PLWSCI to ensure that their concerns and recommendations are addressed. CLINICALTRIAL: ClinicalTrials.gov NCT02582619; https://clinicaltrials.gov/ct2/show/NCT02582619 (Archived by WebCite at http://www.webcitation.org/6mBgcj6z7).
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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 | 0.061 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".