Exploring the Sources and Experiences of Joy in Caregiving: Insights From Formal Caregivers in Long-Term Care
Bibliographic record
Abstract
OBJECTIVES: To explore and understand the sources and experiences of joy in caregiving among formal caregivers in Canadian long-term care (LTC). DESIGN: A qualitative study with interpretative descriptive design. SETTING AND PARTICIPANTS: The participants consisted of 20 formal caregivers from a large public LTC home in British Columbia, Canada, focusing on those with at least 6 months of direct caregiving experience. METHODS: Convenience sampling was conducted to recruit participants. Data were collected through 3 focus groups, with discussions moderated by the primary investigator, and were audio recorded and transcribed. Reflexive thematic analysis was used to identify themes, combining inductive and deductive strategies. To enhance rigor and trustworthiness, the research team engaged in reflective practices, leveraging diverse expertise, and ensuring a rich description of the study context. The study received ethical approval, and participant confidentiality was maintained through pseudonyms. RESULTS: Three interconnected themes of joy in caregiving were identified: (1) Joy in caregiving is a relational and dynamic process that evolves over time and coexists with other emotions, such as sadness and grief. (2) Joy is driven by an attitude shaped by the environment, stemming from an internal attitude, and contributing to a deeper sense of fulfillment despite challenges. (3) Joy in caregiving builds personal team resilience that reduces burnout, fostering compassion and creating a supportive atmosphere through gratitude and shared experiences, benefiting caregivers and residents. CONCLUSIONS AND IMPLICATIONS: This study highlights the relational and evolving nature of joy in caregiving, the influence of internal attitudes and supportive environments, and the impact of joy on resilience and burnout. The findings contribute to characterizing how joy functions within caregiving contexts-specifically for LTC workers-and its broader implications for caregiver well-being and team dynamics.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".