The Role of Self-Compassion in the Lived Experiences of Service Providers Working in Canadian Cancer Support Programs
Bibliographic record
Abstract
Working and providing cancer support services is both challenging and rewarding, yet the lived experiences of the personnel providing such services remain overlooked. It is vital to address the experiences of service providers to better understand the nature of their work and how it may impact their well-being. This paper explores the lived experiences of cancer support service providers, focusing on how they conceptualize and practice self-compassion in their workplace. A qualitative approach centred on phenomenological hermeneutics was utilized to collect in-depth interview data from service providers working in cancer support centres across southwestern Ontario, Canada. The research team used descriptive and narrative analysis to analyze the interview data and produced the following four themes: 1) acknowledging personal limits as a form of self-compassion; 2) organizational support; 3) emotional environment; and 4) prioritized self-care. Recommendations include more comprehensive support systems for service providers, especially if they may be experiencing secondary trauma or compassion fatigue. The findings gleaned from the service providers contribute valuable considerations for both the cancer care workplace and post-secondary institutions. While the findings provide real-life examples of effective support for service providers in the workplace, they also provide important considerations for post-secondary programming that include strategies for balancing empathetic service delivery with exercising self-compassion.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".