EXISTING STRATEGIES FOR ELECTRONIC DATA COLLECTION BY ELDER ABUSE MULTI-DISCIPLINARY TEAMS
Bibliographic record
Abstract
Abstract Elder abuse cases often require integrated responses from social services, medicine, civil legal, and criminal justice. Multi-disciplinary teams (MDTs), which meet periodically to discuss and coordinate interventions for complex cases, have developed in many communities. Little is known about how these MDTs collect case-level data. Our objective was to describe existing strategies of case-level electronic data collection conducted by MDTs across the United States as a preliminary step in developing a comprehensive database strategy. To identify MDTs currently collecting data electronically, we used a snowball sampling approach discussing with national leaders. We also sent an e-mail to the National Center for Elder Abuse listserv inviting participation. We identified and reviewed 11 databases from MDTs. Strategies for and comprehensiveness of data collection varied widely. Databases used ranged from a simple spreadsheet to a customized Microsoft Access database to large databases designed and managed by a third-party vendor. Total data fields collected ranged from 12-338. Types of data included intake/baseline case/client information, case tracking/follow-up, and case closure/outcomes. Information tracked by many MDTs, such as type of mistreatment, was not captured in a single standard fashion. Documentation about data entry processes varied from absent to detailed. We concluded that MDTs currently use widely varied strategies to track data electronically and are not capturing data in a standardized fashion. Many MDTs collect only minimal data. Based on this, we have developed recommendations for a minimum data set and optimal data structure. If widely adopted, this would potentially improve ability to conduct large-scale comparative research.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".