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Record W569877061

Case Management of Long-term Conditions: Principles and Practice for Nurses

2010· book· en· W569877061 on OpenAlexaboutno aff
Janet Snoddon

Bibliographic record

VenueMedical Entomology and Zoology · 2010
Typebook
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsChronic careHealth careMedicineContext (archaeology)Managed careNursingFamily medicinePolitical scienceChronic diseaseGeography
DOInot available

Abstract

fetched live from OpenAlex

Introduction 1 Background to the Implementation of Case Management Modelsfor Chronic Long-Term Conditions within the National HealthService Introduction Primary care management of long-term conditions How management approaches have been developed Developing and delivering care Future of care The impact and cost of chronic disease Identifying patients who require case management National guidelines and evidence-based practice Embedding evidence in practice Making progress in the management of chronic conditions Modernising care in the National Health Service Developing case management and care delivery Case management in the National Health Service Promotion of self-management and self-care Partnerships and expectations Conclusion References 2 Case Management Models: Nationally andInternationally Introduction The context for case management in the NHS Impact of managed care models International models of care reviewed The Alaskan Medical Service Kaiser Permanente (North California) Group Health Cooperative (Seattle, Washington) HealthPartners (Minnesota) Touchpoint Health Plan (Wisconsin) Anthem Blue Cross and Blue Shield (Connecticut) UnitedHealth Europe Evercare Amsterdam HealthCare System (the Netherlands) Outcome intervention model (New Zealand) National model of chronic disease prevention and control(Australia) Guided Care (United States) PACE (United States) Veterans Affairs (Unites States) Improving Chronic Illness Care (Seattle) Expanded Chronic Care Model (Canada) Pfizer (United States) Green Ribbon Health: Medicare in health support What do these models provide? Models in use in England Care management in social care Case management models in the NHS Joint NHS and social care Data for case management Evaluation Conclusion References 3 Competencies for Managing Long-Term Conditions Introduction Development of the competency framework What the competencies are expected to deliver The competencies: what are they? Domain A: advanced clinical nursing practice Domain B: leading complex care co-ordination Domain C: proactively manage complex long-term conditions Domain D: managing cognitive impairment and mentalwell-being Domain E: supporting self-care, self-management and enablingindependence Domain F: professional practice and leadership Domain G: identifying high-risk people, promoting health andpreventing ill health Domain H: end-of-life care Domain I: interagency and partnership working What the competencies aim to do Developing educational models to develop competencies Conclusion References 4 Outcomes for Patients Managing Complex Care Introduction The areas of competence and deliverables for patients/serviceusers: leading complex care co-ordination Identifying high-risk patients, promoting health and preventingill health Interagency and partnership working Conclusion References 5 Outcomes for Patients Advanced NursingPractice Introduction Advanced clinical nursing practice Proactively manage complex long-term conditions Professional practice and leadership Managing care at the end of life Conclusion References 6 Outcomes of Case Management for Social Care and OlderPeople Introduction Policy drivers for the care of older people Health and social care integration Cost of care for older people What do people expect in old age and how will these services becommissioned? What does case management offer to older people? Integrated models of care Impact of case management on older people Managing resources Outcomes for older people Conclusions References 7 Outcomes for Patients Cancer Care and End-of-LifeCare Introduction Gold Standards Framework for Palliative Care Integrated Cancer Care Programme Preparing for the pilot programmes Delivering the pilots Programme outcomes Case Management and ICCP Case management competencies what can/should patientsexpect? The real need for competencies Advanced care planning Preferred place of care and delivering choice programmes Conclusion References 8 Leadership and Advancing Practice Introduction What is leadership? What does leadership provide? Leadership framework in the NHS Skills in leadership Political understanding and functioning Setting targets and delivering outcomes Empowerment and influencing Levels of competence Other leadership frameworks What does good leadership do? Impact on organisations Leadership in case management Leadership and change Leadership is in every role Advanced practice Prescribing Advanced practice in long-term conditions Conclusions References 9 Self Care and Patient Outcomes Introduction What is self-care? Self-care and practitioners Systems for self-care Expert Patient Programme Effectiveness of self-care programmes Promoting self-care: staff role Self-care: models Self-care: the evidence base Using information and technology for self-care How do we engage patients in self-care? Conclusions References 10 What Does this Mean for Patients? Introduction Government expectations What do patients/service users want from care? Reported outcomes from management of long-term conditions Modernisation to enable outcomes for users of services Do patients really see improvement? Understanding the patient/service user experience, how we findout? Public Service Agreement targets Other assessments of user/patient experiences Patient-centred care Allowing patients to tell their tale Outcomes of care and patient experience Experience in case management Partnerships with patients: impact on experience Quality for patients/service users Impact of the provision of information onpatients /service users views and outcomes Conclusions References Index

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.384
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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