LISTENING TO STAKEHOLDERS TO BETTER MANAGE OLDER ADULT MISTREATMENT IN LONG-TERM CARE FACILITIES
Bibliographic record
Abstract
Many types of older adult mistreatment exist in long-term care (LTC) facilities: psychological, physical, sexual, financial, violation of rights, organisational, and ageism. Close to 20% of LTC establishments in the United States are “convicted” of older adult mistreatment each year. In Canada, the problem is also acknowledged by managers and administrators of these types of facilities. However, few studies have described the experience of diverse stakeholders regarding the management of older adult mistreatment situations within LTC facilities. As part of a project using a participative approach to develop and validate a policy template for LTC facilities, 105 key stakeholders (including administrators, managers, long-term care employees, residents/user committees, union representative, complaint commissioners, etc.) were surveyed about the perceived causes, the main difficulties encountered and priorities to address older adult mistreatment in their LTC establishment. The main issues identified were: 1) disparity between the ever-growing needs of residents and the lack of resources; 2) limited knowledge regarding older adult mistreatment and how to identify it properly; 3) a conspiracy of silence and a fear of reporting; 3) non-existent or unclear policies and procedures; as well as 4) no specific person mandated within the facility to respond to mistreatment situations administratively or clinically. Overall, the issues identified by the stakeholders could be addressed with additional training and the implementation of policies and procedures specifically adapted for LTC facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".