A new way of relating: perceptions associated with a team-based error disclosure simulation intervention
Bibliographic record
Abstract
BACKGROUND: Despite the call for open and team-based approaches to error disclosure, the participation beyond physicians and managers is not a common practice in health care settings. Moreover, within the growing literature base on error disclosure, team-based error disclosure is an emerging concept. To address this knowledge gap, a study was undertaken to explore the perceptions associated with an educational simulation intervention for team-based error disclosure. METHODS: A qualitative study that involved analysis of data obtained from semi-structured interviews with a sample of 6 physicians, 6 surgeons, and 12 nurses recruited from the three participating hospitals. RESULTS: Perceptions from study participants elucidated a tension between team-based error disclosure as an unrealistic, forced practice and as a realistic, beneficial practice. This tension was highly contextual and differentiated by study participants' perceptions of the nature of the error; patient's preferences; and prevailing cultural and professional norms. Regardless of the view, study participants described the simulation experience as a new way of relating that departed from existing practice. CONCLUSIONS: Study findings revealed that a team-based approach to disclosure is not realistic or necessary for all error situations, such as when the error involves a single discipline. However, when the error involves a variety of health care professionals interacting with the patient, a team-based approach is beneficial to them and the patient. Further work is required by researchers and administrators to develop and test out interventions that enable health care professionals to practice team-based error disclosure in a safe and supported environment.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".