Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to identify managerial and organizational characteristics and behaviors that facilitate the fostering of a just and trusting culture within the healthcare system. DESIGN/METHODOLOGY/APPROACH: Two studies were conducted. The initial qualitative one was used to identify themes based on interviews with health care workers that facilitate a just and trusting culture. The quantitative one used a policy-capturing design to determine which factors were most likely to predict outcomes of manager and organizational trust. FINDINGS: The factors of violation type (ability vs integrity), providing an explanation or not, blame vs no blame by manager, and blame vs no blame by organization were all significant predictors of perceptions of trust. RESEARCH LIMITATIONS/IMPLICATIONS: Limitations to the generalizability of findings included both a small and non-representative sample from one health care region. PRACTICAL IMPLICATIONS: The present findings can be useful in developing training systems for managers and organizational executive teams for managing medical error events in a manner that will help develop a just and trusting culture. SOCIAL IMPLICATIONS: A just and trusting culture should enhance the likelihood of reporting medical errors. Improved reporting, in turn, should enhance patient safety. ORIGINALITY/VALUE: This is the first field study experimentally manipulating aspects of organizational trust within the health care sector. The use of policy-capturing is a unique feature that sheds light into the decision-making of health care workers as to the efficaciousness of particular managerial and organizational characteristics that impact a just and trusting culture.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".