Professions show different enquiry strategies for elder abuse detection: Implications for training and interprofessional care
Bibliographic record
Abstract
In a project to develop and validate a tool to assist family physicians' identification of elder abuse, nine prospective questions underwent critique and ranking in focus groups comprised of 31 social workers, doctors, and nurses working with elder abuse. Differing attitudes to the questions were discernible amongst the three professions. The social workers' approach appeared based on need to advocate for clients. Nurses' viewpoints seemed influenced by utilitarian concerns for practicality and directness, desire to respect doctors' time constraints, and discomfort that some physicians' questioning might impose on nursing fields of interest. Physicians' concerns tended to be holistic, tempered by practicality and time management issues. However despite such differences expressed during lengthy group discussions, members of all three professions, when asked to independently rank the top five questions, favorably ranked the same five (though not necessarily in the same order). Since there are known barriers to successful elder abuse enquiry the differences and concerns seen in this study may represent another potential obstacle. Programs that address elder abuse might therefore consider sensitizing trainees to the potential predispositions within their own and their colleagues' professions. This proactive strategy might facilitate interprofessional approaches to elder abuse detection.
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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 | 0.075 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".