Emergency department equipment for obese patients: perceptions of adequacy
Bibliographic record
Abstract
AIM: This study reports an investigation to assess patients' and nurses' perceptions of equipment adequacy for obese patients presenting at an emergency department and to assess nurses' knowledge of equipment weight limits in the emergency department. BACKGROUND: The increasing weight of populations in many societies is a challenge to healthcare providers and facilities. Emergency department equipment, specifically, may be inadequate for patient care. METHODS: Two questionnaires were developed. One was administered to 134 emergency department patients with suspected cardiac ischaemia; the other was administered to their respective nurses. Patient and nurse equipment adequacy scores were computed. Patients' self-reported height and weight were used to calculate body mass index. Waist circumference was measured. The data were collected in Canada in 2005. FINDINGS: Patient equipment adequacy scores correlated inversely with both body mass index (r = -0.55, 95% CI = -0.70 to -0.41, P < 0.01) and waist circumference (r = -0.62, 95% CI = -0.75 to -0.48, P < 0.01). Nurse equipment adequacy scores were also inversely related to patient body mass index (r = -0.34, 95% CI = -0.50 to -0.18, P < 0.01) and waist circumference (r = -0.40, 95% CI = -0.56 to -0.24, P < 0.01). There was a weak correlation between nurse and patient equipment adequacy scores (r = 0.27, 95% CI = -0.44 to -0.10, P < 0.01). Small minorities of nurses reported accurate knowledge of weight limits for beds, commodes and toilets. CONCLUSION: Specialized equipment and staff education are needed for adequate management of obese patients in 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".