Intensive care unit professionals’ responses to a new moral conflict assessment tool: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Moral distress is a serious problem for health care personnel. Surveys, individual interviews, and focus groups may not capture all of the effects of, and responses to, moral distress. Therefore, we used a new participatory action research approach-moral conflict assessment (MCA)-to characterize moral distress and to facilitate the development of interventions for this problem. AIM: To characterize moral distress by analyzing responses of intensive care unit (ICU) personnel who participated in the MCA process. RESEARCH DESIGN: In this qualitative study, we invited all ICU personnel at 3 urban hospitals to participate in individual or group sessions using the 8-step MCA tool. These sessions were facilitated by either a clinical ethicist or a counseling psychologist who was trained in this process. During each session, one of the researchers took notes and prepared a report for each MCA which were analyzed using qualitative content analysis. PARTICIPANTS AND RESEARCH CONTEXT: A total of 24 participants took part in 15 sessions, individually or in groups; 14 were nurses and nurse leaders, 2 were physicians, and 8 were other health professionals. ETHICAL CONSIDERATIONS: This study was approved by the Providence Health Care/University of British Columbia Behavioural Research Ethics Board. Each participant provided written informed consent. RESULTS: The main causes of moral distress related to goals of care, communication, teamwork, respect for patient's preferences, and the managerial system. Suggested solutions included communication strategies and educational activities for health care providers, patients, family members, and others about teamwork, advance directives, and end-of-life care. Participants acknowledged that using the MCA process helped them to reflect on their own thoughts and use their moral agency to turn a distressing situation into a learning and improvement opportunity. CONCLUSIONS: Using the MCA tool helped participants to characterize their moral distress in a systematic way, and to arrive at new potential solutions.
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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 | 0.048 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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 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".