Bibliographic record
Abstract
Staff development and training programs focusing on the care of patients with dementia is important in providing staff with the specialized knowledge and skills essential in the management of these patients. In this article, the nature of staff development is explored with an analysis of conceptual visions or definitions of staff development. Secondly, the Cervero (1985) framework for continuing professional education and behavioral change is used as a guide to describe the factors that influence the effectiveness of staff development (i.e., social system, individual learner characteristics, type or design of program). Next, the literature related to the outcomes of staff development programs is analyzed. In particular, do staff development programs lead to increased staff morale and job satisfaction and improved client care? An analysis of orientation and educational programs developed for staff working with patients with dementia in long-term care settings is conducted. Using aspects of the RSA evaluation model (Abruzzese, 1996), each program is examined in terms of its process, content, outcome, and impact, along with its feasibility in clinical settings. This critical analysis provides some direction for the development and evaluation of staff development programs in long-term care settings for those staff who work with patients with dementia.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".