Pilot-testing an applied competency-based approach to health human resources planning
Bibliographic record
Abstract
A competency-based approach to health human resources (HHR) planning is one that explicitly considers the spectrum of knowledge, skills and judgement (competencies) required for the health workforce based on the health needs of the relevant population in some specific circumstances. Such an approach is of particular benefit to planners challenged to make optimal use of limited HHR as it allows them to move beyond simply estimating numbers of certain professionals required and plan instead according to the unique mix of competencies available from the existing health workforce. This kind of flexibility is particularly valuable in contexts where healthcare providers are in short supply generally (e.g. in many developing countries) or temporarily due to a surge in need (e.g. a pandemic or other disease outbreak). A pilot application of this approach using the context of an influenza pandemic in one health district of Nova Scotia, Canada, is described, and key competency gaps identified. The approach is also being applied using other conditions in other Canadian jurisdictions and in Zambia.
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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 | 0.017 | 0.024 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".