Bibliographic record
Abstract
Purpose This paper aims to describe how Covenant Health has developed an ongoing talent pool of health‐care aides (HCAs) to staff its health‐care facilities at St Therese Villa (STV), a designated assisted‐living (DAL) seniors' care facility in Southern Alberta. Design/methodology/approach The paper explains the reasons for the initiative, the form it takes and the results it is achieving. Findings The paper reveals that the STV training program starts with 15 days in a classroom dealing with proper lifting procedures, medication delivery and caring for dementia patients. Each candidate then job shadows other experienced HCAs for 12 shifts. They are evaluated by their future peers and the experienced HCAs submit written evaluations. Successful candidates are then offered a casual HCA position and begin covering shifts. They then serve a 500‐hour probationary period and complete a recognized HCA certificate through an accredited Canadian college. Practical implications The paper explains that the program allows STV consistently to have a pool of people available who are trained exactly how the facility wants them to be. It anticipates turnover and provides recruits in advance of that turnover, who can step into a position immediately. This process also allows employees to learn and grow in a safe environment with constant supervision around them. Social implications The paper highlights how a similar type of training program could be adapted in some other high‐labor‐turnover fields. Originality/value The paper provides the inside story of a successful succession‐planning initiative.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".