Practising Open Disclosure: clinical incident communication and systems improvement
Bibliographic record
Abstract
This article explores the way that professionals are being inducted into articulating apologies to consumers of their services, in this case clinicians apologising to patients. The article focuses on the policy of Open Disclosure that is being adopted by health care organisations in the US, Canada, the UK and Australia and other nations. Open Disclosure policy mandates 'open discussion of clinical incidents' with patient victims. In Australia, Open Disclosure policy implementation is currently being complemented by intensive staff training, involving simulation of apology scenarios with actor-patients. The article presents an analysis of data collected from such training sessions. The analysis shows how simulated apologising engages frontline staff in evaluating the efficacy of their disclosures, and how staff may thereby be inducted into reconciling their affective and reflexive sensibilities with their organisational and professional responsibilities, and thereby produce the required organisational apology. The article concludes that Open Disclosure, besides potentially relaxing tensions between clinicians and consumers, may also affect how staff experience and enact their role in the overall system of health care organisation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".