Risking vulnerability: Enacting moral agency in the is/ought gap in mental health care
Bibliographic record
Abstract
AIM: To explore how healthcare providers in acute care mental health settings navigate ethically challenging situations, enact moral agency, practice in congruence with ethical standards and mitigate moral distress (MD). DESIGN: Grounded theory, a qualitative methodology. METHODS: Over 18 months between 2015 and 2017, we reviewed documents, conducted observations and interviewed multidisciplinary participants (N = 27) from inpatient and emergency departments. Participants either provided direct care (N = 14) or were in leadership positions (N = 13). Data were analysed iteratively using constant comparison, coding, memoing and theorizing, which continued until saturation was reached in July 2016. FINDINGS: The basic social process of how healthcare professionals enacted moral agency, Risking Vulnerability, occurred in the context of Systemic Inhumanity, a constant source of MD. Participants Risked Vulnerability, balancing professional obligations, clinical expertise and organizational processes with their own vulnerability in the system as they strove to practice ethically. Risking Vulnerability was composed of Pushing Back, Working Through Team Relationships and Struggling with Inhumanity. CONCLUSION: Healthcare professionals' moral agency occurred at the nexus of structure (organizational constraints) and agency (persons). Given this, interventions for MD should be directed at all levels of healthcare to support moral agency, promote ethical practice and improve care. IMPACT: Sociopolitical elements such as austerity measures undermined ethical practice at the level of direct care. Enactment of moral agency is dynamic, influencing experiences of MD: participants supported by leadership or colleagues to enact moral agency noted that they were not stuck in MD. Interventions supporting moral agency throughout the healthcare system are necessary to mitigate experiences of MD. Findings enhance our understanding of the role of action in the experience of MD.
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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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.055 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.007 |
| 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".