Bibliographic record
Abstract
When patients suffer harm arising from their caregiver it is common that patients or family members will demand that we, the regulators, sanction the caregiver. That desire to assign blame and seek punishment is very strong, especially when it appears that the caregiver has made an error or directly caused the harm.In the spring of 2004 two patients died in Calgary, Alberta, from hyperkalemia as a result of the incorrect mixing of dialysate solution. The Calgary Health Region, the entity responsible for the provision of hospital care, responded promptly, disclosing the facts about these cases to the two families, conducted a critical incident review and sought an independent external review. Among its recommendations, the external reviewers made reference to the “just culture” and wrote: “In a just culture, workers can differentiate what is acceptable and unacceptable behavior. A just culture recognizes that in most cases punishing staff for errors does nothing to help ensure that the next employee in a similar situation will not make the same error. At the same time a just culture does not accept negligence, willful violations of rules and standards or substance abuse on the job. Healthcare workers expect management to act when it is warranted and may even feel more vulnerable when unacceptable behavior is not penalized.”Their report included the recommendations to incorporate patient safety as a core value and guiding principle and to create a clear policy on the consequences of reporting of errors (i.e., reporting will not have a negative impact on the individual’s performance appraisal and will not lead to disciplinary action).As regulators we become the interface between the public and the health care system when we are asked to investigate a complaint where a patient has suffered harm. In protecting the public we have a duty to ensure that unsafe, incompetent or impaired physicians are prevented from injuring patients – by limiting their practices, removing them from practice or ensuring they receive treatment or remediation. We also have a responsibility to understand patient safety principles, to acknowledge that harm to a patient is seldom a result of a single act by an unsafe practitioner. More often it is the result of a series of errors (Reason’s “Swiss cheese” model) and reflects underlying vulnerabilities in our systems of care.Accepting that the majority of medical errors resulting in harm are a result of system problems (rather than individual fallibility) is, as Lucian Leape says, a “transforming concept” and obliges us to move from the traditional name and blame approach to a learning organization approach – trying to understand how the event occurred rather than by trying to identify a perpetrator.The concept of a “just culture” offers us a touchstone for our work: Punishing a practitioner for harming a patient is unlikely to uncover the defects in the system that lead to the event and will not prevent recurrence; punishment as an approach is likely only to cover up errors and system vulnerabilities and discourage reporting of close calls and events that do lead to harm; and punishment reinforces the outdated view that the cause of patient harm is imperfect individuals, rather than unsafe systems of care. I think we all accept that very few practitioners go to work with the intention of harming patients.However, there are some acts and behaviors that are willful, egregious and unsafe, and for which visible and measured action must be taken. Impaired and unwell physicians should receive treatment and, once recovered, require monitoring of their health and their practice. Physicians who deliberately break rules – such as boundary violators – should receive appropriate sanctions for their actions.From my perspective as a regulator, the “just culture” concept helps define the intersection of medical regulation and health care safety principles and offers us – and the public we serve – a rationale for our approach to medical error and patient harm. We cannot tolerate deliberately unsafe violations and breaches of ethical principles or standards of practice, and at the same time we must understand the system in which our members work if we are to fairly adjudicate on the conduct of physicians when patients suffer harm. To do otherwise would not only be unfair to physicians and other health care providers, but would impede the work that continues to make our systems of care safer.
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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".