A Continuing Educational Program Supporting Health Professionals to Manage Grief and Loss
Bibliographic record
Abstract
Health professionals working in oncology face the challenge of a stressful work environment along with impacts of providing care to those suffering from a life-threatening illness and encountering high levels of patient loss. Longitudinal exposure to loss and suffering can lead to grief, which over time can lead to the development of compassion fatigue (CF). Prevalence rates of CF are significant, yet health professionals have little knowledge on the topic. A six-week continuing education program aimed to provide information on CF and support in managing grief and loss and consisted of virtual sessions, case-based learning, and an online community of practice. Content included personal, health system, and team-related risk factors; protective variables associated with CF; grief models; and strategies to help manage grief and loss and to mitigate against CF. Participants also developed personal plans. Pre- and post-course evaluations assessed confidence, knowledge, and overall satisfaction. A total of 189 health professionals completed the program (90% nurses). Reported patient loss was high (58.8% > 10 deaths annually; 12.2% > 50). Improvements in confidence and knowledge across several domains (p < 0.05) related to managing grief and loss were observed, including use of grief assessment tools, risk factors for CF, and strategies to mitigate against CF. Satisfaction level post-program was high. An educational program aiming to improve knowledge of CF and management of grief and loss demonstrated benefit.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".