Cultivating the conditions for care: it's all about trust
Bibliographic record
Abstract
This perspective article shares the viewpoints of two long-standing patient safety advocates who have participated first-hand in the evolution of patient engagement in healthcare quality and safety. Their involvement is motivated by a rejection of the common cruelty of institutional betrayal that compounds harm when patient safety fails. The advocates have sought to understand how it can be that fractured trust spreads so predictably after harm, just when it most needs strengthening. Instead, the abandonment of trust upends healthcare values and effectiveness at interpersonal, systemic and structural levels. They argue that authentic care (healthcare that is truly caring) transcends mere service delivery, thus embodying an inviolable commitment to mutual well-being, compassion and generosity. The advocates identify the influence of social determinants, such as culture, identity, and socioeconomic status, as critical to trust formation, where pathogenic vulnerability exacerbates existing inequalities and further impedes trust. The advocates call for a shift from transactional to relational, trust-based interactions that explore the potential for mobilizing restorative justice principles to repair harm and rebuild trust, enabling dialogue, mutual understanding and systemic improvement. Trust, they assert, is born in relationships, not transactions. The bureaucratic, legal and resource constraints that often impair meaningful interactions, also cause moral distress to healthcare providers and poor care quality for patients. They argue that central to the current healthcare crisis is the fundamental need for genuine connection and trust, framing this as both a practical necessity and a confirmation of humanity as intrinsic to healthcare. The advocates envision a future where patient engagement is integral to patient safety to prioritize epistemic justice, mutual respect and compassionate care, to restore healthcare as a cohesive, supportive and deeply human endeavor. They query what contributions a restorative approach could make to centre trust as necessary for cultivating the conditions for care in our healthcare system.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.090 |
| Scholarly communication | 0.021 | 0.032 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".