Bibliographic record
Abstract
* Foreword * SECTION 1. INTRODUCTION TO CULTURE AND VALUES IN LONG-TERM CARE * Unloving Care Revisited: The Persistence of Culture * Managing Organizational Culture Change: The Case of Long-Term Care * Culture Change in Long-Term Care Facilities: Changing the Facility or Changing the System? * The Historical Context of Humanistic Culture Change in Long-Term Care * Leading Culture Change in Long-Term Care: A Map for the Road Ahead * Achieving Organizational Change Within the Context of Cultural Competence * The Changing Consumer: The Social Context of Culture Change in Long-Term Care * Family Caregivers, Health Care Professionals, and Policy Makers: The Diverse Culture of Long-Term Care * Pioneer Network: Changing the Culture of Aging in America * SECTION 2. MODELS OF CULTURE CHANGE IN LONG-TERM CARE * Evolution of Eden * Culture Change in Long Term Care: The Wellspring Model * Models for Individuals with Alzheimer Disease: Beyond the Special Care Framework * Peer Mentoring of Nursing Home CNAs: A Way to Create a Culture of Caring * The Live Oak Regenerative Community: Championing a Culture of Hope and Meaning * SECTION 3. CASE STUDIES: IMPLEMENTING CHANGE * Teresian House--Using the Environment to Support Cultural Change * Center for Nursing and Rehabilitation--Culture Change in an Urban Environment * The Providence Mount St. Vincent Experience * Organizational Culture and Bathing Practice: Ending the Battle in One Facility * The Power of Circles: Using a Familiar Technique to Promote Culture Change * SECTION 4. CASE STUDIES: CULTURE CHANGE BRIEFS * Introduction to Case Studies: Culture Change Briefs * Apple Health Care: Culture Change in a Privately Owned Nursing Home Chain * Case Study Brief: The Lyngblomsten Care Center, St. Paul, MN * SECTION 5. AN INTERNATIONAL PERSPECTIVE * Using Strengths-Based Practice to Support Culture Change: An Australian Experience * Implementing the Eden Alternative(R) in Australia * Beyond the Medical Model--The Eden Alternative(R) in Practice: A Swiss Experience * Changes in Long-Term Care for Elderly People with Dementia: A Report from the Front Lines in British Columbia, Canada * SECTION 6. IS CHANGE REALISTIC? * Quality Oversight and Culture Change in Long-Term Care * Policy Values and Culture Change in Long-Term Care--The Role of State Government in Catalyzing Change * Selecting a Model or Choosing Your Own Culture * Successfully Surviving Culture Change * Index * Reference Notes Included
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".