Role of Educational Institutions in Identifying and Responding to Emerging Health Human Resources Needs
Bibliographic record
Abstract
The healthcare system continues to evolve, requiring innovation to promote patient-centred, fiscally responsible healthcare delivery. This evolution includes changes to the skills and competencies required of the health human resources (HHR), both regulated and unregulated, who are central supports to healthcare delivery. This has become a priority agenda item at the international, national, provincial, regional and local levels. This paper describes the system factors that drive the emergence of HHR skill and competency needs, and explores the roles of various institutions in the identification of and response to HHR needs. Educational institutions play an important role in responding to emerging HHR needs. Their actual response to HHR skill and competency needs will ultimately depend on the risk posed to the organizations of either addressing, or not addressing, these needs. These decisions are complex and are balanced against strategic, operational and educational risks, benefits and realities within each given educational institution. Educational institutions - through their linkages with the workplace, industry, professional organizations and government - have a unique view and understanding of many facets of the complexity of HHR planning. This paper proposes that educational institutions play a pivotal role as levers in a more coordinated response to emerging HHR needs and, as such, should be intimately involved in comprehensive HHR 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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".