Development of an interactive model for planning the care workforce for Alberta: case study
Bibliographic record
Abstract
INTRODUCTION: In common with other jurisdictions, Alberta faces challenges in ensuring a balance in health worker supply and demand. As the provider organization with province-wide responsibility, Alberta Health Services needed to develop a forecasting tool to inform its position on key workforce parameters, in the first instance focused on modeling the situation for Registered Nurses, Licensed Practical Nurses and health care aides. This case study describes the development of the model, highlighting the choices involved in model development. CASE DESCRIPTION: A workforce planning model was developed to test the effect of different assumptions (for instance about vacancy rates or retirement) and different policy choices (for example about the size of intakes into universities and colleges, different composition of the workforce). This case study describes the choices involved in designing the model. The workforce planning model was used as part of a consultation process and to develop six scenarios (based on different policy choices). DISCUSSION AND EVALUATION: The model outputs highlighted the problems with continuation of current workforce strategies and the impact of key policy choices on workforce parameters. CONCLUSIONS: Models which allow for transparency of the underlying assumptions, and the ability to assess the sensitivity of assumptions and the impact of policy choices are required for effective workforce planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".