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1. |
Record Nr. |
UNINA9910139965003321 |
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Titolo |
CPD for non-medical prescribers [[electronic resource] ] : a practical guide / / edited by Marion Waite and Jan Keenan |
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Pubbl/distr/stampa |
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Chichester, West Sussex ; ; Ames, Iowa, : Blackwell Pub., 2010 |
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ISBN |
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1-282-37957-7 |
9786612379574 |
1-4443-1772-5 |
1-4443-1773-3 |
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Descrizione fisica |
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1 online resource (258 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Nurses - Prescription privileges - Great Britain |
Drugs - Prescribing - Great Britain |
Medicine - Study and teaching (Continuing education) - Great Britain |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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CPD for Non-Medical Prescribers; Contents; List of Contributors; Acknowledgements; Introduction; Section One: General Principles for Continuing Professional Development for Non-Medical Prescribers; 1 Keeping Up to Date with Legal and Professional Frameworks for Non-Medical Prescribing; Introduction; The law as it applies to medicines; The law as it applies to non-medical prescribing roles; Professional standards as applied to non-medical prescribing; Conclusion; References; 2 Prescribing Practice from the Employer's Perspective: The Rationale for CPD within Non-Medical Prescribing |
IntroductionThe organisational importance of continuing professional development; Continuing education and continuing professional development; The manager's obligation to provide continuing professional development; Identifying and meeting local learning needs; Professional guidance; Organisational responsibility - the role of the Trust's non-medical prescribing lead; Meeting organisational and |
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individual needs for CPD; Identifying opportunities for CPD; Monitoring CPD as part of appraisal; Maintaining the service; Conclusion; References; Useful websites |
3 Writing and Maintaining a Non-Medical Prescribing Policy for Your OrganisationIntroduction; Background to clinical governance; Developing the policy; Clinical governance; Patient information; Selection of potential prescribers; Monitoring practice; Organisational roles and responsibilities; Useful contacts; Final section; Conclusion; References; 4 Organising CPD for Non-Medical Prescribers at a Regional Level; Introduction; The structure of the NHS within the United Kingdom; The national context for the development of non-medical prescribing |
The role of a regional non-medical prescribing facilitatorThe role of Trust NMP leads; Organising CPD via a local forum; Delivering CPD via a local forum; Reflection: providing CPD for non-medical prescribers; Where are we now?; Commissioning CPD for non-medical prescribers; Conclusion; References; Section Two: Speci.c Approaches to CPD for Non-Medical Prescribers; 5 Using E-learning for CPD within Non-Medical Prescribing; Introduction; Background; How can learning technologies be used in practice?; Using a virtual learning environment (VLE); Planning a blended learning activity |
When things do not go wellWeb 2.0 technologies; Electronic portfolios; Review of National Prescribing Centre online resources for non-medical prescribers; Other online resources; Building and sharing your own database of online prescribing resources; Conclusion; References; Useful websites; 6 Action Learning and Learning Sets; Introduction; Action learning; Who will benefit from action learning?; What kind of organisation makes action learning a success?; Putting action learning into practice; Practical experience - learning sets in a single speciality |
Additional benefits of action learning and learning sets |
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Sommario/riassunto |
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In this new era of healthcare, the importance of Continuing Professional Development cannot be underestimated. Non-Medical Prescribers have a responsibility to themselves, their employer and their patients to keep up-to-date with developments in this fast-moving area of healthcare. This book looks at the current context of CPD in this area and provides guidance for facilitation. The book is divided into three clear sections. The first looks at general principles of CPD and considers overarching and organisational issues such as clinical governance. The second section looks at specific appro |
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2. |
Record Nr. |
UNINA9910254843703321 |
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Autore |
Chaudhuri Arindam |
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Titolo |
Bankruptcy Prediction through Soft Computing based Deep Learning Technique / / by Arindam Chaudhuri, Soumya K Ghosh |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XVII, 102 p. 59 illus.) |
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Disciplina |
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Soggetti |
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User interfaces (Computer systems) |
Artificial intelligence |
Computer simulation |
Management information systems |
Computer science |
Banks and banking |
Statistics |
User Interfaces and Human Computer Interaction |
Artificial Intelligence |
Simulation and Modeling |
Management of Computing and Information Systems |
Banking |
Statistics for Business, Management, Economics, Finance, Insurance |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique.- Datasets Used -- Experimental Results -- Conclusion . |
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Sommario/riassunto |
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This book proposes complex hierarchical deep architectures (HDA) for predicting bankruptcy, a topical issue for business and corporate institutions that in the past has been tackled using statistical, market-based and machine-intelligence prediction models. The HDA are |
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formed through fuzzy rough tensor deep staking networks (FRTDSN) with structured, hierarchical rough Bayesian (HRB) models. FRTDSN is formalized through TDSN and fuzzy rough sets, and HRB is formed by incorporating probabilistic rough sets in structured hierarchical Bayesian model. Then FRTDSN is integrated with HRB to form the compound FRTDSN-HRB model. HRB enhances the prediction accuracy of FRTDSN-HRB model. The experimental datasets are adopted from Korean construction companies and American and European non-financial companies, and the research presented focuses on the impact of choice of cut-off points, sampling procedures and business cycle on the accuracy of bankruptcy prediction models. The book also highlights the fact that misclassification can result in erroneous predictions leading to prohibitive costs to investors and the economy, and shows that choice of cut-off point and sampling procedures affect rankings of various models. It also suggests that empirical cut-off points estimated from training samples result in the lowest misclassification costs for all the models. The book confirms that FRTDSN-HRB achieves superior performance compared to other statistical and soft-computing models. The experimental results are given in terms of several important statistical parameters revolving different business cycles and sub-cycles for the datasets considered and are of immense benefit to researchers working in this area. |
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