MCRFMarshfield Clinic Research Foundation (Wisconsin)
MCRFMedicinal Cannabis Research Foundation (UK)
MCRFMy Chemical Romance Forum
MCRFMidwest Cardiovascular Research Foundation (Davenport, IA)
MCRFMicroscopy and Cytometry Resource Facility (John Curtin School of Medical Research)
MCRFMaterials Combustion Research Facility (NASA Marshall Space Flight Center)
MCRFMomentarily Comoving Reference Frame
MCRFMineral Core Research Facility (Alberta Geological Survey; Canada)
MCRFMahaska Community Recreation Foundation (Oskaloosa, IA)
MCRFMissing Child Reporting Form
MCRFMadison Capital Revolving Fund
MCRFMinerals Characterisation Research Facility (University of Queensland; Australia)
MCRFMount Cameroon Research Foundation (UK)
MCRFMulti-Cell Radial Flow
MCRFMemorial Cancer Research Foundation (Los Angeles, CA)
MCRFMinnesota Colon and Rectal Foundation (St. Paul, MN)
MCRFMajor Central Research Facilities
MCRFMail Code Request Form
MCRFMicronesian Collection Reference Files
MCRFMaster Course Reference File
MCRFMajor Contingency Response Force
MCRFMuscatine Coronary Risk Factor
MCRFMining Community Reserve Fund (Canada)
MCRFModified Current Ratio Factor
MCRFMunicipal Capital Reserve Fund
MCRFMarshfield Clinical Research Foundation (Marshfield, Wisconsin)
MCRFMaryland Cigarette Restitution Fund
MCRFMindanao Communications Resource Foundation (Philippines)
MCRFMinnesota Counties Research Foundation (St. Paul, MN)
MCRFmild chronic renal failure
MCRFManchester Community Research Factory (UK)
MCRFMaster Course Record Form
MCRFManagement Centre Research Fund
MCRFMicronesian Collection Reference File
MCRFMotion Capture Research Facility
MCRFMobile Club Repair Forum
MCRFMaster Cross Reference File
MCRFMicrosoft Customer Reporting Framework
MCRFMissing Children's Recovery Foundation (Bayshore, NY)
MCRFMontana College Republican Federation
MCRFMomentarily Comoving Referential Frame
MCRFmoderate chronic renal failure
MCRFMedical Center of the Rockies Foundation
MCRFMaterials Composting and Recovery Facility
MCRFMotorcycle Club Running Free
MCRFMission Configuration Requirements File
MCRFMedication Center Review Form
MCRFModule Class Record Form
MCRFMultiple Cell Radial Filter
MCRFmonoclonal rheumatoid factors
References in periodicals archive ?
Similarly, because it quickly became evident that the program required active involvement by the research team, a research liaison visited each RC/AL setting on a weekly basis to promote use of the MCRF, answer questions, and address concerns.
Barriers to program adoption identified by RC/AL staff included staff turnover; the limited number of supervisory staff available to train new staff; the limited number of staff qualified/allowed to use the MCRF, because most staff lacked medical training; the lack of physician involvement in the program; and the fact that medical care was typically provided off-site.
Identified challenges to implementation included NH policy and practices (especially related to the use of the MCRF), resident or family concerns, and staff turnover and resistance to change.
Despite the limited provider training achieved in RC/AL settings, and little use of the MCRF in NHs, both settings evidenced change in antibiotic prescribing.
(28) At the same time, RC/AL health care supervisors considered clinical care coordination and communication to be an important component of their role, (29) which is consistent with their receptivity to the MCRF. Training RC/AL staff to better communicate with medical providers, as was done with the MCRF, may well be the most effective way to improve medical care in these settings; further, it may ultimately improve the satisfaction of all stakeholders.
(19,21,23) In this project, the MCRF was modified according to staff request, while still maintaining the integrity of the signs and symptoms related to infections.
The MCRF theory solved this problem and other related issues that hindered conditional Markov chain simulations on sparse sample data.
A MCRF refers to a random field defined by a single spatial Markov chain that moves or jumps in space and decides its state at any uninformed (i.e., unobserved and unvisited in a simulation process) location by interactions with its nearest neighbors in different directions and its last stay (i.e., visited) location [17].
The prior probability indicates the single Markov chain nature of a MCRF. The likelihood component is composed of multiple terms (one for each nearest neighbor), which update the prior probability using nearest neighbors in different directions by a manner of recursion as follows:
Therefore, a MCRF model can be explained from the viewpoint of Bayesian inference.
Such a simplified Co-MCRF model provides the MCRF approach the capability of dealing with large data sets.