Disability Training for Health Workers: A Global Narrative Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Health worker training on disability is a recognized component of achieving high standards of health for people with disabilities, given that health worker’s lack of knowledge, stigma, and negative attitudes towards people with disabilities act as barriers to high quality health care. Objective To understand the published literature on training health workers about disability. Methods We searched five databases for relevant peer-reviewed articles published between January 2012 and January 2021. Studies that focused on training health care workers to improve knowledge, confidence, self-efficacy, and competence to support people with physical, sensory, or intellectual impairments were included. Data about the details of the intervention (setting, participants, format, impact assessments, etc.) and its effects were extracted. Results There is an array of highly local tools to train health workers across stages of their training and careers (pre-service, in-service, and continuing professional development). Studies involving people with disabilities in the training, community placements, simulations, or interactive sessions were found to be most effective in improving knowledge, confidence, competency, and self-efficacy. Conclusions As part of initiatives to build inclusive health systems and improve health outcomes for people with disabilities, health workers around the world need to receive appropriate and evidence-based training that combine multiple methods and involve people with disabilities.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".