Return to Work following Ill Health or Disability in a Public-Private Health Care Facility: A Study in South Africa
Bibliographic record
Abstract
INTRODUCTION: Disability management involves is dynamic interactional strategies used to promote an individual’s return to work. These strategies revolve around the person’s health condition and contextual factors for example their employer and the work environment. However, there remains limited literature on the strategies used in the public healthcare sector. Objective: To explore the return to work strategies used at a public sector facility in the province of KwaZulu Natal, South Africa. METHOD: A case study design, with multiple sources of data contributed towards profiling disability management strategies implemented at a central quaternary health care facility. Data collection methods included a file audit, work ability index assessments and semi-structured interviews with employees. Saturation sampling was used to recruit n = 23 employees who had been referred for occupational therapy vocational assessments over a period of 10 years. Data from the file audit were analysed using descriptive statistics and interviews were analysed using thematic analysis. RESULTS: Fifty six percent (n = 38) of the participants that were currently employed at the institution scored between 28 and 38 (moderate) on the Work Ability Index and required job realignments and reasonable accommodations within their current vocations. Twenty two percent (n = 5) scored 7–9 (poor) and were medically boarded or on long-term incapacity leave. CONCLUSIONS: Occupational therapists play a significant role in disability management within public health care facilities. Return to work strategies and reasonable accommodations can improve productivity in the workplace.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".