Emergency Department Staff Knowledge and Practice of Caring for Individuals with Intellectual Disabilities Seeking Mental Health Care
Bibliographic record
Abstract
Individuals with intellectual disabilities have limited access to out-patient community-based mental health care. Emergency departments have become a fundamental entry for patients with intellectual disabilities to receive mental health care, leaving patients with intellectual disabilities being frequent users of the emergency department when compared to those without intellectual disabilities. The purpose of this study is to examine and unpack the knowledge, practice, and professional experience of emergency department staff regarding patients with intellectual disabilities seeking mental health care. The study aims to contribute to understandings about the ways in which emergency department staff utilize the emergency department space, time, and resources, and how this utilization impacts how responsive and supportive care is provided to patients with intellectual disabilities. Semi-structured face-to-face and phone interviews were conducted with seven emergency department social workers, registered nurses, and physicians from four emergency departments in Calgary, Canada. A qualitative descriptive study design was employed to collect, analyze, and describe and interpret findings. Emergency department staff described: the emergency department standard practice and procedures, environment, conflicting demands, professional and personal experience in the emergency department, and the ways these descriptions helped or hindered their ability to provide responsive and supportive care to patients with intellectual disabilities. Guided by a critical disability theoretical framing and the author’s experience as a disability service professional, the author has unpacked descriptive findings to illuminate that: the emergency department is not the place for any patient with or without an intellectual disability to receive adequate mental health care, the emergency department prioritizes physical health over mental health, emergency department staff do not have adequate time, space, resources, and academic or clinical training to provide responsive and supportive care, further illuminating how the knowledge, practice, and professional experience of emergency department staff additionally contribute to the marginalization of individuals with intellectual disabilities in various systems and practices, particularly the emergency department.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".