Datasets:

nuhmanpk
/

firstaid-treatment-instruct

dataset_info:
  features:
    - name: messages
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
    - name: source
      dtype: string
    - name: topic
      dtype: string
  splits:
    - name: train
      num_bytes: 73571533
      num_examples: 71037
    - name: eval
      num_bytes: 8174614
      num_examples: 7893
  download_size: 74336872
  dataset_size: 81746147
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: eval
        path: data/eval-*
task_categories:
  - question-answering
  - reinforcement-learning
  - text-generation
size_categories:
  - 10K<n<100K
tags:
  - healthcare
  - first-aid
  - emergency-response
  - instruction-tuning
  - supervised-finetuning
  - rag
  - medical-qa
  - grounded-generation
  - safety
  - domain-specific-llm

FirstAid-Treatment IT

Dataset Description

FirstAid-Treatment IT is an instruction tuning and question-answering dataset for training domain-specialized language models on first aid, emergency response, procedural medical support, and safety guidance.

The dataset consists of grounded conversational training examples built from source-aligned first-aid reference material and emergency response documentation.

It is designed for:

Supervised Fine-Tuning (SFT)
Question Answering (QA)
Retrieval-Augmented Generation (RAG)
Grounded medical assistant training
Safety-focused instruction-following models

Supported Tasks

This dataset supports several NLP tasks:

1. Instruction Fine-Tuning

Examples are formatted as chat-style instruction data:

Summarize this passage
Extract procedural steps
List key takeaways
Answer grounded domain questions

Suitable for fine-tuning:

:contentReference[oaicite:2]{index=2} Llama models
:contentReference[oaicite:3]{index=3} models
:contentReference[oaicite:4]{index=4} Qwen models
Medical domain assistants

2. Question Answering

Supports:

Extractive QA
Grounded QA
Procedural QA
Multi-step reasoning QA

Example tasks:

Question: What should be prioritized in massive hemorrhage response?

Answer: Follow immediate bleeding control protocols, prioritize life-threatening hemorrhage intervention, and escalate emergency care.

3. Retrieval-Augmented Generation

Useful for:

Grounded response generation
Hallucination reduction
Citation-aware medical assistants
Retrieval evaluation benchmarks

Dataset Summary

Total rows: 78,930
Train split: 71K+
Format: Parquet
Modality: Text
Message-format conversational samples
Source-grounded instruction-response data

Topics Covered

Includes diverse first aid topics such as:

Trauma management
Bleeding control
Poisoning response
Respiratory emergencies
Pediatric emergencies
Disaster response
Toxic exposure
Tactical casualty care
Infection control
Crowd crush and stampede support
Hazardous materials response
Radiation exposure first aid
Decompression and dive injuries
Neonatal emergencies
Mental health crisis support

Dataset Structure

Each example follows chat-style instruction tuning format:

{
 "messages": [
   {
      "role":"system",
      "content":"You are a domain expert assistant. Answer only from provided context."
   },
   {
      "role":"user",
      "content":"Extract procedural steps..."
   },
   {
      "role":"assistant",
      "content":"1. Assess scene safety..."
   }
 ],
 "source":"firstaid_114_massive_hemorrhage_march.pdf_chunk_449",
 "topic":"massive hemorrhage management"
}

Features

Feature	Type
messages	list
source	string
topic	string

Dataset Creation

Source Data

Built from structured first aid and emergency care reference materials, guidelines, and procedural source documents.

Examples are generated from source chunks using grounded instruction synthesis patterns:

summarization prompts
procedural extraction prompts
knowledge QA prompts
key takeaway generation

Data Generation Pipeline

High-level pipeline:

Source ingestion
Document chunking
Topic extraction
Instruction synthesis
Grounded answer generation
Quality filtering
Conversational formatting

Intended Uses

Recommended:

✅ SFT for medical assistants
✅ Domain adaptation
✅ Grounded QA systems
✅ RAG benchmarking
✅ Safety-aligned assistant training
✅ Medical instruction following research

Out-of-Scope Uses

Not intended for:

Real-time emergency diagnosis
Clinical decision making
Autonomous medical advice
Replacement for licensed professionals
Unsupervised deployment in high-risk settings

This is a training dataset, not medical advice.

Bias, Risks, and Limitations

Limitations

Coverage limited to included sources
May not reflect jurisdiction-specific protocols
Not a substitute for updated clinical guidance
Synthetic instruction generation may introduce formatting bias

Safety Risks

Models trained solely on this data may:

Overgeneralize procedures
Omit escalation contexts
Produce incomplete emergency recommendations

Human oversight is strongly recommended.

Fine-Tuning Example

from datasets import load_dataset

ds = load_dataset(
    "nuhmanpk/FirstAid-Treatment_it"
)

print(ds["train"][0]["messages"])

Example SFT formatting:

tokenizer.apply_chat_template(
  sample["messages"],
  tokenize=False
)

Works well for chat-style SFT pipelines using:

TRL SFTTrainer
Axolotl
Unsloth
Transformers Trainer

Benchmark / Evaluation Ideas

Can be evaluated on:

Exact Match (EM)
F1 for QA
Faithfulness
Groundedness
Hallucination rate
Instruction-following accuracy

Potential benchmark tasks:

Procedural step extraction
Emergency response QA
Safety reasoning
Medical RAG evaluation

Citation

@dataset{nuhmanpk_firstaid_treatment_it,
 author = {Nuhman PK},
 title = {FirstAid-Treatment IT},
 year = {2026},
 publisher = {Hugging Face},
 url = {https://huggingface.co/datasets/nuhmanpk/FirstAid-Treatment_it}
}

Creator

Created by Nuhman PK