Long‑Term Impact of HEAT Educational Intervention in the Emergency Department in Karachi, Pakistan
Bibliographic record
Abstract
Background: Karachi faced an unprecedented heatwave in 2015, causing severe health outcomes. The heat emergency awareness and treatment (HEAT) intervention was developed to train healthcare providers to identify and manage heat‑related illnesses (HRIs). The HEAT intervention was implemented in major emergency departments (EDs) in Karachi in 2018. Objective: This study evaluated the long‑term impact of the HEAT intervention on ED physicians’ diagnosis and management of patients with HRIs in a single tertiary‑care hospital. Method: This study utilized time‑series analyses to evaluate the long‑term impact of HEAT intervention utilizing ten‑year data (pre‑intervention, 2013–2017 and post‑intervention, 2018–2022). Data were obtained from a single hospital related to diagnoses and management of HRIs for the study period. The outcomes assessed were the number of HRIs diagnosed, use of intravenous (IV) fluids, and use of sponging and ice packs. A zero‑inflated interrupted time series Poisson regression model was used to assess the impact of HEAT intervention on diagnosis and management of HRIs, while accounting for time and maximum ambient temperature. Findings: At the crude level, analyses showed a decrease in the number of HRI diagnoses (estimate = −1.63, p < 0.001*), use of IV fluids (estimate = −0.72, p = 0.09), and in the use of sponging (estimate = −0.51, p = 0.64) in the post‑intervention period. Findings from the sensitivity analyses, excluding the outlier observations due to the severe heat event of 2015, showed a statistically significant increase in HRI diagnoses (estimate = 2.18, p < 0.001*) and in the use of IV fluids (estimate = 2.07, p < 0.001*) in the post‑intervention period. Conclusion: Our educational training intervention was effective in improving HRI diagnosis and management among ED physicians from a select hospital over a long‑term period. Findings need to be generalized with caution to other settings.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".