Improvement in the diagnosis and practices of emergency healthcare providers for heat emergencies after HEAT (heat emergency awareness & treatment) an educational intervention: a multicenter quasi-experimental study
Bibliographic record
Abstract
BACKGROUND: The incidence of heat emergencies, including heat stroke and heat exhaustion, have increased recently due to climate change. This has affected global health and has become an issue of consideration for human health and well-being. Due to overlapping clinical manifestations with other diseases, and most of these emergencies occurring in an elderly patient, patients with a comorbid condition, or patients on poly medicine, diagnosing and managing them in the emergency department can be challenging. This study assessed whether an educational training on heat emergencies, defined as heat intervention in our study, could improve the diagnosis and management practices of ED healthcare providers in the ED setting. METHODS: A quasi-experimental study was conducted in the EDs of four hospitals in Karachi, Pakistan. Eight thousand two hundred three (8203) patients were enrolled at the ED triage based on symptoms of heat emergencies. The pre-intervention data were collected from May to July 2017, while the post-intervention data were collected from May to July 2018. The HEAT intervention, consisting of educational activities targeted toward ED healthcare providers, was implemented in April 2018. The outcomes assessed were improved recognition-measured by increased frequency of diagnosing heat emergencies and improved management-measured by increased temperature monitoring, external cooling measures, and intravenous fluids in the post-intervention period compared to pre-intervention. RESULTS: Four thousand one hundred eighty-two patients were enrolled in the pre-intervention period and 4022 in the post-intervention period, with at least one symptom falling under the criteria for diagnosis of a heat emergency. The diagnosis rate improved from 3% (n = 125/4181) to 7.5% (n = 7.5/4022) (p-value < 0.001), temperature monitoring improved from 0.9% (n = 41/4181) to 13% (n = 496/4022) (p-value < 0.001) and external cooling measure (water sponging) improved from 1.3% (n = 89/4181) to 3.4% (n = 210/4022) (p-value < 0.001) after the administration of the HEAT intervention. CONCLUSION: The HEAT intervention in our study improved ED healthcare providers' approach towards diagnosis and management practices of patients presenting with health emergencies (heat stroke or heat exhaustion) in the ED setting. The findings support the case of training ED healthcare providers to address emerging health issues due to rising temperatures/ climate change using standardized treatment algorithms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".