A qualitative co-design-based approach to identify sources of workplace-related distress and develop well-being strategies for cardiovascular nurses, allied health professionals, and physicians
Bibliographic record
Abstract
OBJECTIVE: Clinician distress is a multidimensional condition that includes burnout, decreased meaning in work, severe fatigue, poor work-life integration, reduced quality of life, and suicidal ideation. It has negative impacts on patients, providers, and healthcare systems. In this three-phase qualitative investigation, we identified workplace-related factors that drive clinician distress and co-designed actionable interventions with inter-professional cardiovascular clinicians to decrease their distress and improve well-being within a Canadian quaternary hospital network. METHODS: Between October 2021 and May 2022, we invited nurses, allied health professionals, and physicians to participate in a three-phase qualitative investigation. Phases 1 and 2 included individual interviews and focus groups to identify workplace-related factors contributing to distress. Phase 3 involved co-design workshops that engaged inter-professional clinicians to develop interventions addressing drivers of distress identified. Qualitative information was analyzed using descriptive thematic analysis. RESULTS: Fifty-one clinicians (24 nurses, 10 allied health professionals, and 17 physicians) participated. Insights from Phases 1 and 2 identified five key thematic drivers of distress: inadequate support within inter-professional teams, decreased joy in work, unsustainable workloads, limited opportunities for learning and professional growth, and a lack of transparent leadership communication. Phase 3 co-design workshops yielded four actionable interventions to mitigate clinician distress in the workplace: re-designing daily safety huddles, formalizing a nursing coaching and mentorship program, creating a value-added program e-newsletter, and implementing an employee experience platform. CONCLUSION: This study increases our understanding on workplace-related factors that contribute to clinician distress, as shared by inter-professional clinicians specializing in cardiovascular care. Healthcare organizations can develop effective interventions to mitigate clinician distress by actively engaging healthcare workers in identifying workplace drivers of distress and collaboratively designing tailored, practical interventions that directly address these challenges.
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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 | 0.049 | 0.037 |
| 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 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".