Specialist Rehabilitation Providers’ Experiences with an Online Self- Compassion Course: A Reflexive Thematic Analysis (Preprint)
Bibliographic record
Abstract
Background: Rehabilitation health care providers (HCPs) report high levels of burnout. Self-compassion interventions have shown beneficial effects on HCP burnout, but they have never been explored in specialist rehabilitation settings where challenges may differ. Objective: This study aimed to explore the experiences of specialist rehabilitation HCPs with an online Self-Compassion for Healthcare Communities (SCHC) course aimed at providing tools to regulate emotional well-being, including burnout. Methods: Semistructured qualitative interviews (n=20) with specialist rehabilitation HCPs were used to explore experiences with the SCHC course. A reflexive thematic analysis study design was chosen to highlight participants' insights, and inductive coding was undertaken to analyze data and organize findings into themes. Results: Six themes that reflected HCPs' experiences with the course were constructed: (1) the nature of working in rehabilitation; (2) different perspectives on burnout in specialist rehabilitation; (3) a new perspective, less self-criticism, more self-compassion; (4) growing recognition of the importance of compassion for oneself and others; (5) challenges engaging with the SCHC course; and (6) challenges to sustaining self-compassion in specialist rehabilitation. Conclusions: Specialist rehabilitation HCPs who participated in the SCHC course developed a new understanding of the importance of fostering self-compassion and compassion for others. This shift may have supported HCPs in navigating workplace challenges, and HCPs described experiencing changes that could help with burnout, improve their care of patients and relationships with colleagues.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".