Determining Possible Professionals and Respective Roles and Responsibilities for a Model Comprehensive Elder Abuse Intervention: A Delphi Consensus Survey
Bibliographic record
Abstract
OBJECTIVE: We have undertaken a multi-phase, multi-method program of research to develop, implement, and evaluate a comprehensive hospital-based nurse examiner elder abuse intervention that addresses the complex functional, social, forensic, and medical needs of older women and men. In this study, we determined the importance of possible participating professionals and respective roles and responsibilities within the intervention. METHODS: Using a modified Delphi methodology, recommended professionals and their associated roles and responsibilities were generated from a systematic scoping review of relevant scholarly and grey literatures. These items were reviewed, new items added for review, and rated/re-rated for their importance to the intervention on a 5-point Likert scale by an expert panel during a one day in-person meeting. Items that did not achieve consensus were subsequently re-rated in an online survey. ANALYSIS: Those items that achieved a mean Likert rating of 4+ (rated important to very important), and an interquartile range<1 in the first or second round, and/or for which 80% of ratings were 4+ in the second round were retained for the model elder abuse intervention. RESULTS: Twenty-two of 31 recommended professionals and 192 of 229 recommended roles and responsibilities rated were retained for our model elder abuse intervention. Retained professionals were: public guardian and trustee (mean rating = 4.88), geriatrician (4.87), police officer (4.87), GEM (geriatric emergency management) nurse (4.80), GEM social worker (4.78), community health worker (4.76), social worker/counsellor (4.74), family physician in community (4.71), paramedic (4.65), financial worker (4.59), lawyer (4.59), pharmacist (4.59), emergency physician (4.57), geriatric psychiatrist (4.33), occupational therapist (4.29), family physician in hospital (4.28), Crown prosecutor (4.24), neuropsychologist (4.24), bioethicist (4.18), caregiver advocate (4.18), victim support worker (4.18), and respite care worker (4.12). CONCLUSION: A large and diverse group of multidisciplinary, intersectoral collaborators was deemed necessary to address the complex needs of abused older adults, each having important roles and responsibilities to fulfill within a model comprehensive elder abuse intervention.
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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 | 0.130 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".