Nursing care for youth with intellectual and developmental disabilities in the times of COVID-19: Toward a Nursing Practice Competency Framework
Bibliographic record
Abstract
Background: Persons with intellectual and developmental disabilities (IDD) face interlocking inequities in access to healthcare services. Nurses require education and training in specific competencies to address their needs. This cross-sectional study explored the experiences and perspectives of youth with IDD on nursing care in the times of COVID-19 restrictions across 4 provinces in Canada. Methods: Nine youth (16-29 years) completed a virtual survey: without assistance of caregiver parent (n=3), supported by a parent (n=3), and represented by parent proxy (n=3). Close-ended Likert-type questions assessed health, mental health, and Quality of Care and Support (QOCS). Open-ended questions addressed Person-and-Family-centred Care (PFCC), communication and behaviour management. At the analytical stage the qualitative and quantitative data were integrated applying concurrent mixed-methods principles. Results: Emerging themes include: 1) Limited access to quality of care (e.g., dismissive attitude, safety concerns, insufficient respite funding, unaffordable care, shortage of nurses prepared in developmental disability, complicated program enrolment process, insufficient knowledge on IDD rights and services, competency and professionalism); 2) Person-and-Family-Centered Care (e.g., experiences of PFCC, not a Person-Centered care; 3) Communication is key to quality nursing care; and 4) Inadequate emotional support (e.g., respect and value youths’ wishes, nurses need emotional support too). Conclusion: Barriers to quality nursing care for youth with IDD include lack of communication skills, emotional support, and Person-and-Family-Centered Care. Nursing care tailored to the needs of persons with IDD is crucial for their health and wellbeing and requires structural changes at the macro (enhanced funding for program/services), meso (education, curricula, training), and individual levels (awareness).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".