“It's soul destroying to be honest”: A qualitative study of morally uninhabitable working environments and the responsibilization of healthcare professionals working in concurrent disorders
Bibliographic record
Abstract
Healthcare professionals' wellbeing is crucial for healthcare system functioning and population health. Prolonged moral distress may result in burnout, undermining healthcare professionals' wellbeing, negatively impacting quality of patient care, and resulting in considerable healthcare system costs. We conducted 36 interviews with healthcare professionals working with patients with concurrent mental and substance use disorders in British Columbia, Canada, exploring their perceptions of the institutional constraints which shaped their experiences and caring practices. We conducted a reflexive thematic analysis of the interviews guided by the theoretical concept of responsibilization. While most participants found their work rewarding, many encountered institutional constraints which limited their ability to provide the standard of care to which they felt morally and ethically obligated. Prolonged exposure to such morally uninhabitable working environments resulted in participants' moral distress and burnout. Central to participants' narratives was the role of responsibilization in both contributing to and exacerbating moral distress and burnout. On top of their caring duties, participants perceived a need to over-function to offset institutional constraints within healthcare, and address their and their colleagues' moral distress and burnout without adequate institutional support. Findings demonstrate how individual-centered interventions are inadequate without proper institutional support, and have the potential to re-enact rather than rectify moral distress and burnout. Structural interventions are paramount to redress these occupational harms and protect healthcare professionals’ wellbeing. • Responsibilization drives moral distress and burnout in healthcare professionals • They must overcompensate to address institutional constraints • The act of overcompensating undermines their ability to provide care • This contributes to their experiences of moral distress and burnout • They must also address their moral distress and burnout due to limited supports
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 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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".