Global health and innovation: A panoramic view on health human resources in the COVID‐19 pandemic context
Bibliographic record
Abstract
While policy-makers in many jurisdictions are paying increasing attention to health workforce issues, human resources remain at best only partially aligned with population health needs. This paper explores the governance of human resources during the pandemic, looking at the Quebec health system as a revelatory case. We identify three issues related to health human resource (HHR) policies: working conditions, recognition at work and scope of practice. We empirically probe these issues based on an analysis of popular media, policy reports and participant observation by the lead authors in various forums and research projects. Using an integrated model of HHR, we identify major vulnerabilities in this domain. Persistent labour shortages, endemic deficiencies in working environments and inequity across occupational categories limit the ability to address critical HHR issues. We propose three ways to eliminate HHR vulnerabilities: reorganize work through participatory initiatives, implement joint policy making to rebalance power across the health workforce, and invest in the development of capacities at all system levels.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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".