Evaluating the effectiveness of cardiac arrest resuscitation short course (CARS) for rural physicians of Asia: The Rural Emergency Care Training for Physicians (RECTIFY) project
Bibliographic record
Abstract
BACKGROUND: Physicians from resource-constrained rural areas being lone lifesavers pose a unique challenge in resuscitating emergencies like cardiac arrest. Rural Emergency Care Training for Physicians (RECTIFY) was devised as a short course training to equip them to deal with occasional emergencies using minimal gadgets. This study was conceived to assess the effectiveness of the RECTIFY-Cardiac Arrest Resuscitation Short course (CARS) module in improving current knowledge and practice of cardiopulmonary resuscitation (CPR) among interested rural physicians of Asia. METHODS: A three-tier observational study was conducted to assess current CPR knowledge with a pretested structured questionnaire and skills using a checklist, followed by a 3-h hands-on training and posttest evaluation using the same study instruments. Data were entered into Microsoft Excel and analyzed using SPSS 13.0. RESULTS: = 0.001). Whereas a majority improved upon chest compression skills, appropriate use of sophisticated gadgets like automated external defibrillators (AED) was low (2.4%) despite training. CONCLUSION: The level of knowledge and skill among participants was poor despite the enthusiasm and positive intent. The impact of RECTIFY-CARS on knowledge and skills among participant physicians was significant and is recommended for implementation by health policymakers in resource-poor rural settings. However, essential gadgets like AED were not impactful which necessitates the use of simpler rural alternatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".