System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: We derive a significant portion of our revenue from third-party payers, including the Medicare and Medicaid programs. Changes in these government programs in recent years have resulted in limitations on reimbursement and, in some cases, reduced levels of reimbursement for healthcare services. Payments from federal and state government programs are subject to statutory and regulatory changes, administrative rulings, interpretations and determinations, requirements for utilization review, and federal and state funding restrictions, all of which could materially increase or decrease program payments, as well as affect the cost of providing service to patients and the timing of payments to facilities. We are unable to predict the effect of recent and future policy changes on our operations. In addition, the uncertainty and fiscal pressures placed upon federal and state governments as a result of, among other things, deterioration in general economic conditions and the funding requirements from the federal healthcare reform legislation, may affect the availability of taxpayer funds for Medicare and Medicaid programs. In addition, the vast majority of the net revenues generated at our behavioral health facilities located in the United Kingdom are derived from governmental payers. If the rates paid or the scope of services covered by governmental payers in the United States or United Kingdom are reduced, there could be a material adverse effect on our business, financial position and results of operations.
Assistant: