Bibliographic record
Abstract
Abstract Retaining and empowering staff in long-term services and supports (LTSS) is essential for sustaining quality care. Despite high demands, staff often receive low wages and limited recognition, making workplace culture, professional growth, and well-being critical factors in job satisfaction/retention. This symposium presents findings from three qualitative studies examining staff experiences in LTSS, highlighting factors fostering retention, resilience, and joy in caregiving. The first study explored retention factors among Canadian LTSS staff through focus groups, identifying three key themes: (1) professional growth and development through training and mentorship, (2) recognition and workplace culture that foster team cohesion and appreciation, and (3) relational joy in caregiving, where meaningful connections with residents and colleagues enhanced resilience. The second study analyzed essays from LTSS clinicians reflecting on moments of joy in their work. Content analysis revealed that joy stemmed from (1) building relationships through residents’ unique life stories, (2) teamwork that fostered belonging, and (3) shared decision-making that supported person-centered, end-of-life care. Despite workforce shortages, clinicians found fulfillment through narrative medicine, celebrating direct care staff, and engaging in palliative care discussions. The third study examined experiences of LTSS administrators during the COVID-19 pandemic. Findings underscored the importance of communication, collaboration, and relational leadership in sustaining staff morale. Innovative communication strategies helped alleviate isolation and reinforced a sense of team belonging. Collectively, these studies in Canada and the US underscore the need for evidence-based retention strategies that prioritize professional development, workplace recognition, and relational well-being to sustain and strengthen the joy of the LTSS workforce.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".