Using experience-based co-design approach to address mental health issues among healthcare workers and leaders in long-term care
Bibliographic record
Abstract
Introduction: Burnout remains a critical issue within the long-term care (LTC) sector, underscoring the urgent need for early detection and prevention strategies targeting the healthcare workforce. This paper aimed to describe the processes of uncovering the underlying drivers of burnout and distress among LTC workers, providing a foundational understanding to inform the development of a Burnout Assessment Tool (BAT). Materials and methods: Using an experience-based co-design approach, 11 focus groups were held with a purposive sample of 24 Ontario LTC workers between July 2023 and October 2024. Each session, lasting 2 h, included 4-5 participants representing a diverse range of professional and occupational roles, including personal support workers, nurses, social workers, and administrators or leaders. The objectives of this approach were two-fold: (1) to explore the work-related experiences of LTC workers; and (2) to examine their perceptions of existing burnout and distress tools, including the Maslach Burnout Inventory-Human Services Survey, to assess their relevance and applicability within the LTC context. Each focus group session was audio-recorded and transcribed verbatim. An iterative process generated codes from the transcripts that culminated in a thematic framework of key findings. Results: Four interrelated themes emerged: (1) challenges inherent in the LTC work environment; (2) the impacts of workplace pressures on employee health and well-being; (3) managing psychosocial risk factors in the workplace; and (4) the need for a context-specific tool to assess burnout in LTC settings. Participants provided in-depth perspectives on their roles within the LTC sector, highlighting the profound impact of burnout on their well-being and the persistent challenges they face in their work environment. Discussion/conclusion: The findings highlight the pressing need for tailored systemic interventions to effectively address burnout and moral distress among LTC workers and leaders. By employing a co-design approach, this study offers critical insights into the lived experiences of these workers, informing the development and refinement of burnout assessment tools to better reflect the unique needs of this workforce. Developing a BAT, co-created by and for LTC workers, coupled with robust support systems, is crucial to addressing the rising distress and fostering resilience within this vital sector.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".