Management of Agitation in Emergency Medical Services for Older Adults: A Qualitative Exploration
Bibliographic record
Abstract
INTRODUCTION: Emergency medical services (EMS) providers are often first responders to agitated older adults, providing critical clinical care and transport. However, significant knowledge gaps persist in our understanding of agitation management for older adults in the prehospital setting. AIMS: To describe the barriers and facilitators to the management of agitation in older adults and the reduction of restraint use by EMS providers. METHODS: In-depth semi-structured qualitative interviews (n = 30) took place with EMS providers employed in Alberta, Canada. The theoretical domains framework (TDF) served as a guiding structure for the development of the interview guide. Framework analysis was used to analyze the qualitative data: a line-by-line thematic analysis was used to identify codes/themes, which were then mapped onto the TDF, and behavior change wheel. RESULTS: Six major thematic categories were identified. EMS providers reported inadequate training and support, especially for managing agitation in older adult populations. Restraints are used as a safety measure for patient and provider safety, and as a last resort once other agitation management strategies have been exhausted. EMS providers report a complex decision-making matrix of balancing the risks, benefits, and ethical considerations of restraint use, which is often collaborative and integrates EMS protocols. Common barriers to effective agitation management in EMS, as well as non-restraint agitation management techniques are also discussed. CONCLUSION: The present study is the first in-depth exploration of EMS provider experiences regarding the management of agitation and chemical and physical restraints in older adults.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".