“Pain is not typically taken into consideration due to him being nonverbal”- emergency department experiences among persons with disabilities: a mixed methods study in Kingston, Ontario
Bibliographic record
Abstract
Background Persons with disabilities (PWD) are more likely to visit the emergency department (ED) and often have complex health needs when accessing care in the ED. Yet there is limited understanding of ED care experiences among PWD, especially in a Canadian context. The aim of this study was to examine the ED care experiences of PWD in contrast to a comparison group in Kingston, Ontario to better understand their health care needs. Methods A mixed-methods study with a community-based participatory approach examining participants’ past ED care experiences (within 24 months) was conducted in Kingston, ON. Quantitative data from those with disabilities and those from the comparison group were compared using chi squared tests to identify differences between groups. An inductive and deductive thematic analysis approach was used to identify themes in the shared qualitative data. Convergence of findings across quantitative and qualitative data was undertaken. Results A total of 175 participants identified as having a disability. In contrast with the comparison group (N = 949), PWD were more likely to report being given too little attention to their needs (p < 0.001), that it was more important to be treated with kindness/respect than to receive the best possible medical care (p < 0.001), to report feelings of disrespect and/or judgement (p < 0.001), and that better understanding of personal identity/situation/culture and better communication would improve ED care. Qualitative analysis highlighted the following themes: poor communication between PWD and health care providers (HCP), compassionate medical care received, perceived HCP negative attitudes/beliefs related to having a disability and substance misuse, and perceived HCP lack of knowledge/skill to treat the unique health needs of PWD. Conclusion Findings highlight the need to improve ED care for PWD. Future quality improvement initiatives should focus on incorporating a deeper understanding of disability into medical education and emergency medicine (EM) residency education, designing curricula that emphasize cultural humility, and implementing community-based placements providing opportunities for health professionals to work with and learn from PWD.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".