Mental health and public sector healthcare: international case studies
Bibliographic record
Abstract
The mental health of the health care workforce has been deteriorating globally over many years and is primarily related to increased psychosocial risks leading to increasing work-related stress due to the deterioration of healthcare work environments. This has led to a global healthcare workforce crisis due to global shortages of healthcare workers, a crisis which has been developing over several decades and in most countries, including low-, middle- and high-income countries. The Covid-19 crisis exacerbated both the demands on national health care systems and staff shortages, with large numbers of staff sick or in quarantine. The primary cause has been decades of under-resourcing of national healthcare systems related to the dissemination and implementation of neoliberal policy frameworks that have undermined the provision of public services, including healthcare. This report examines the links between healthcare workers mental health and the rise in psychosocial risks across high, medium and low income countries with specific case studies of Sweden, Australia, Canada, Brazil and Liberia . It includes a critique of global healthcare recruitment by high income countries from middle and low income countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".