A DIVERSITY TRAINING EVALUATION FRAMEWORK FOR THE COMMUNITY HEALTH AND AGED CARE SECTOR
Bibliographic record
Abstract
program consisted of multiple sessions over a 2-day period for staff as well as an open public lecture for family, friends and others who were interested in the program.We collected survey data from 44 LTC staff (e.g., personal support workers, licensed nurses, recreation staff), 25 in Ontario and 19 in Saskatchewan; and 44 family members and others (n= 21 from Ontario, 23 from Saskatchewan).The majority of participants rated the training program as excellent, stating "it's just basic human care".All participants stated that they now understand the purpose of Namaste Care.Most participants stated that they learned how to interact with residents in the Namaste room and the types of programming that are offered.Similarly, participants who attended the public lecture stated that they were very satisfied with the education, stating that the public lecture helped them learn more about how the program can be implemented.Participants in both groups suggested having follow-up sessions with a 'report back' about how the program impacts resident outcomes.These study findings support the use of a facility-wide educational program to help launch a new innovation in LTC.
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 | 0.122 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".