Bibliographic record
Abstract
Abstract Fifteen years ago Rovner and Katz (1993) declared that nursing homes “are the modern mental institutions for the elderly, but the training of staff and physicians, processes of care, and the recognition and treatment of mental disorders lag behind the current state of scienti1c knowledge.” Indeed, lack of trained staff continues to be frequently cited as one of the key problems in the care of nursing home residents. Recent guidelines recommend that long-term care homes “should have an education and training program for staff related to the needs of residents with depression and/or behavioral concerns” (Canadian Coalition for Seniors’ Mental Health, 2006). There is evidence that poor education and training can compromise resident care and safety (Anderson et al., 2005). This chapter will explore some of the key issues related to the education of staff in the long-term care setting. In exploring this issue it is important to bear in mind that although education is necessary, it is often not suf1cient to improve clinical practice. Enabling care providers to make the transition from “knowing” to “doing” is complex and multifaceted, and the process of successful knowledge transfer and knowledge utilization will vary among different practice settings.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".