Health self‐advocacy training for persons with intellectual disabilities
Bibliographic record
Abstract
BACKGROUND: People with intellectual disabilities (ID) have unequal access to health care. While systemic efforts are addressing health inequalities, there remains a need to demonstrate that persons with ID can increase their health self-advocacy skills. METHOD: A randomised control design with up to 6-month follow-up was used to evaluate the 3Rs (Rights, Respect and Responsibility) health self-advocacy training program for persons with ID (n = 31). Training involved teaching participants to recognise and redress health rights violations in the context of respect and responsibility. Training materials included PowerPoint slides and interactive video scenarios illustrating health rights, respect and responsibility problem and non-problems. Two-hour training sessions were conducted twice a week in a group format where participants played a game and answered questions. RESULTS: The health rights training group made significantly more correct responses on post training and follow-up tests than the control group. Training effects generalised to untrained scenarios and in situ health interviews. CONCLUSIONS: The results of this study suggest that persons with ID can learn complex skills related to health self-advocacy. More research is needed to improve in situ generalisation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".