Elder abuse and mistreatment : policy, practice, and research
Bibliographic record
Abstract
* About the Contributors * Foreword * Preface * Introduction (Patricia Brownell) * OVERVIEW AND POLICY * Communities Respond to Elder Abuse (Lisa Nerenberg) * A Policy Perspective on Elder Justice Through APS and Law Enforcement Collaboration (Christopher Dubble) * Social Inclusion: An Interplay of the Determinants of Health--New Insights into Elder Abuse (Elizabeth Podnieks) * PRACTICE * Self-Determination and Elder Abuse: Do We Know Enough? (L. Rene Bergeron) * Use of a Single Page Elder Abuse Assessment and Management Tool: A Practical Clinician's Approach to Identifying Elder Mistreatment (Patricia A. Bomba) * An Elder Abuse Shelter Program: Build It and They Will Come: A Long Term Care Based Program to Address Elder Abuse in the Community (Daniel A. Reingold) * Consumer Fraud and the Elderly: A Review of Canadian Challenges and Initiatives (Carole A. Cohen) * Psycho-Educational Support Groups for Older Women Victims of Family Mistreatment: A Pilot Study (Patricia Brownell and Deborah Heiser) * RESEARCH * Ethical and Psychosocial Issues Raised by the Practice in Cases of Mistreatment of Older Adults (Marie Beaulieu and Nancy Leclerc) * Elder Abuse and Neglect Among Veterans in Greater Los Angeles: Prevalence, Types, and Intervention Outcomes (Ailee Moon, Kerianne Lawson, Maria Carpiac, and Eleanor Spaziano) * Hearing the Voices of Abused Older Women (Jill Hightower, M. J. (Greta) Smith, and Henry C. Hightower) * Effects of Dependency on Compliance Rates Among Elder Abuse Victims at the New York City Department for the Aging, Elderly Crime Victim's Unit (Mebane E. Powell and Jacquelin Berman) * Index * Reference Notes Included
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".