Examples of co-payment in the following topics:
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- Some of the essential terms associated with health insurance are premiums, deductibles, co-payments, and explanations of benefits.
- A co-payment is the amount that an insured person must pay out of pocket before the health insurer pays for a particular visit or service.
- For example, an insured person might pay a $45 co-payment for a doctor's visit, or to obtain a prescription.
- A co-payment must be made each time a particular service is obtained.
- It also explains how payment amounts and patient responsibility amounts have been determined.
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- Figure 3.2 is an example of a binary (as opposed to a signed or ordinal or valued) and directed (as opposed to a co-occurrence or co-presence or bonded-tie) graph.
- Figure 3.3 is an example of a "co-occurrence" or "co-presence" or "bonded-tie" graph that is binary and undirected (or simple).
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- Both A and B are "co-present" or "co-occurring" in the relation of "having a conversation. " Or, we might also describe the situation as being one of an the social institution of a "conversation" that by definition involves two (or more) actors "bonded" in an interaction (Berkowitz).
- "Simple" or "Co-occurrence" or "co-presence" or "bonded-tie" graphs use the convention of connecting the pair of actors involved in the relation with a simple line segment (no arrow head).
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- Each tie or relation may be directed (i.e. originates with a source actor and reaches a target actor), or it may be a tie that represents co-occurrence, co-presence, or a bonded-tie between the pair of actors.
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- In human context, a family is a group of people affiliated by consanguinity, affinity, or co-residence.
- In human context, a family is a group of people affiliated by consanguinity, affinity, or co-residence.
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- For one example, relations among a set of actors might (in some populations) be coded as either "nuclear family co-member" or "co-workers" or "extended family member" or "co-religionist" or "none."
- For another example, we could combine multiple relations to create qualitative types: 1 = kin only, 2 = co-worker only, 3 = both kin and co-worker, and 4 = neither kin nor co-worker.
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- Using information about the co-variation among the multiple measures, we can infer an underlying dimension or factor; once we've done that, we can locate our observations along this dimension.
- Similarly, we could "scale" the events in terms of the patterns of co-participation of actors -- but weight the actors according to their frequency of co-occurrence.
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- The result is showing that there is a cluster of issues that "co-occur" with a cluster of donors - actors defining events, and events defining actors.
- Event coordinates for co-participation of donors in California initiative campaigns
- Actor coordinates for co-participation of donors in California initiative campaigns
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- The members are listed in the top part of the output; the bottom part of the output shows the same result in matrix form, with "1" indicating co-presence in a weak component, and "2" indicating co-presence in a strongly transitive component.