‘No more heroes’: The <scp>ILC</scp> Oxford Statement on fundamental care in times of crises
Bibliographic record
Abstract
AIM: To outline the International Learning Collaborative (ILC) Oxford Statement, explicating our commitment to ensuring health and care systems are equipped to meet patients' fundamental care needs during times of unprecedented crisis. DESIGN/METHOD: Discussion paper. The content was developed via a co-design process with participants during the ILC's international conference. KEY ARGUMENTS: We, the ILC, outline what we do and do not want to see within our health and care systems when faced with the challenges of caring for patients during global pandemics and other crises. Specifically, we want fundamental care delivery to be seen as the minimum standard rather than the exception across our health and care systems. We want nursing leaders to call out and stand up for the importance of building fundamental care into systems, processes and funding priorities. We do not want to see the voices of nursing leaders quashed or minimized in favour of other agendas. In turn, what we want to see is greater recognition of fundamental care work and greater respect for the people who do it. We expect nurses to have a 'seat at the table' where the key health and care decisions that impact patients and staff are made. CONCLUSION: To achieve our goals we must (1) ensure that fundamental care is embedded in all health and care systems, at all levels; (2) build on and strengthen the leadership skills of the nursing workforce by clearly advocating for person-centred fundamental care; (3) co-design systems that care for and support our staff's well-being and which foster collective resilience rather than overly rely on individual resilience; (4) improve the science and methodologies around reporting and measuring fundamental care to show the positive impact of this care delivery and (5) leverage the COVID pandemic crisis as an opportunity for transformational change in fundamental care delivery.
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 | 0.113 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".