Caring for Those Who Care
Bibliographic record
Abstract
Since the events of 9/11, health care facilities have devoted substantial resources to emergency preparedness, especially for a surge of patients in a large-scale incident. Hurricane Katrina reinforced the need for such surge planning. Due to the SARS experience in Toronto, health care professionals have had increased awareness of their “duty-to-care” responsibility. These caregivers make the decision, even when they themselves may be at risk, to continue to care for patients. However, little has been done about planning to care for these caregivers. Health care professionals can be deeply affected physically, emotionally, and spiritually when caring for patients in a large-scale incident. Emergency preparedness professionals must consider the needs of health care providers because providers must care for a large number of patients with limited resources under stressful conditions. It is the obligation and responsibility of each health care organization to care for these caregivers. However, when assigning responsibility for this task, it becomes evident this responsibility belongs to employee health nurses, “employee advocates,” and organizational leaders.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.025 | 0.014 |
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".