Evaluation of a co‐designed Health Check‐in for adults with intellectual and developmental disabilities and family caregivers to support pandemic recovery
Bibliographic record
Abstract
Abstract Background The COVID‐19 pandemic has brought about disruptions in healthcare for adults with intellectual and developmental disabilities. There is a need to explore ways to promote proactive healthcare and better prepare individuals for healthcare encounters. Methods A co‐designed tool, the COVID Check‐in Tool, was introduced as part of a virtual health education programme to encourage proactive healthcare. Implementation of this Health Check‐in was evaluated with 36 adults with intellectual and developmental disabilities and 96 family caregivers who completed the programme using surveys, structured interviews and focus groups. Findings Forty‐four percent of participants engaged in the Health Check‐in process, resulting in many reported benefits for those who participated. However, there were also barriers to initiating the Check‐in, along with challenges using the COVID Check‐in Tool, according to both the adults with disabilities who were interviewed and the family caregivers. Conclusions The study underscores the importance of considering ways to integrate tools into routine healthcare practices, to facilitate improved healthcare delivery for people with intellectual and developmental disabilities during pandemic recovery efforts. As well, involving people with lived experience in the development and implementation of healthcare resources is critical.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".