Bibliographic record
Abstract
Throughout the COVID-19 pandemic, frontline healthcare workers around the globe provided exceptional patient care despite fears of infection, shortages of staff and supplies, and the frustrations of trying to treat a novel pathogen. At the same time, COVID-19 exposed deep and systemic risks to healthcare team members' physical, psychological, and emotional safety driving burnout to crisis levels. Burnout is arising not only from the emotional toll of caring for sick and dying patients, but COVID-19 also exposed flaws in our health system and infrastructure. Systemic inequities were amplified as COVID-19 disproportionately impacted people of colour and Indigenous community members. A renewed and expanded definition of safety is needed to restore trust, recruit, and retain individuals to the healing professions, enable care to be provided with the greatest skill and humanity, and ensure the well-being of every person working in healthcare. In collaboration with CEOs of a diverse group of health systems in the United States, the author drafted a Declaration of Principles that expands the definition of safety to include safeguarding psychological and emotional well-being of team members, promoting health justice by declaring equity and anti-racism as core components of safety, and ensuring physical safety, which includes a zero-harm program to eliminate workplace violence, both physical and verbal. We invite Canadian leaders to embrace these concepts and commit to supporting team member safety and well-being as an essential foundation for public health. We must humanize healthcare and the time to act is now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.053 |
| Scholarly communication | 0.026 | 0.035 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.025 | 0.052 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".