Complementary Multidisciplinary Elder Abuse Service in A Geriatric Clinic.
Bibliographic record
Abstract
Aim: To describe a pilot, innovative intervention project combining the adoption and adaptation for hospital use of a screening instrument designed for use in primary health care settings in Canada (and translated into 6 other languages) and a dialogical narrative therapeutic approach. The development of a complementary multidisciplinary elder abuse service (CMEAS) in a private-public partnership. Material and Methods: Between June 2015 – March 2016 elderly hospitalized in Iasi town, Geriatric Clinic and suspected of being abused had the benefit of a complementary multidisciplinary elder abuse service (CMEAS) after being screened for abuse, neglect or abandonment experiences. Results: A total of 450 patients admitted to the Geriatric Clinic were invited for the study and 152 raised suspicion of abuse experiences and were screened with EASI. Of these patients, 132 where found positive and were invited to participate in CMEAS. Conclusions: Such a multidisciplinary service requires the collaboration between the geriatric team (medical service), psychologist, social worker, legal advisor, and psychiatrist, referral of cases to relevant public and private community services and their monitoring after hospital discharge throughout project duration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".