SECONDARY TRAUMATIC STRESS: DEVELOPING AND IMPLEMENTING A PROGRAM TO BUILD RESILIENCE IN CHILD MALTREATMENT FELLOWS
Bibliographic record
Abstract
Abstract BACKGROUND Fellows in child maltreatment paediatrics (CMP) are at risk of secondary traumatic stress (STS). Contributing factors include inexperience, younger age, lack of mastery, high caseloads, and longer working hours. Literature has previously focused on individual resilience; as understanding about STS evolves, it has been recognized that organizational resilience is important in supporting healthcare professionals. OBJECTIVES Our objective was to develop, implement, and evaluate an innovative program to increase resilience to STS among fellows in a child maltreatment training program. DESIGN/METHODS A trauma-informed counsellor with expertise in both secondary-traumatic stress and medical education was identified through the University Wellness Office. The counsellor was not a member of the CMP team and facilitated a targeted program that included monthly, small group session for all CMP fellows. Sessions involved low-intensity activities that encouraged self-reflection and focused attention. The counsellor facilitated discussions around difficult cases with active listening, immersion into the affective experience of others, and avoidance of judgment, blame, or criticism. Fellows were encouraged to speak about their own experiences, rather than commenting on the experiences of others. This created a safe environment in which to explore and process difficult material. Evidence-based strategies were offered at the end of each session. An important component of the program was an iterative process of feedback and reflection on the session structure and process. Written reflections were collected from fellows and staff, which were qualitatively analyzed by two reviewers to identify key themes. RESULTS Qualitative analysis of individual written reflections identified four major themes, including high satisfaction with program, strategies for prevention and management of STS, bonded fellowship peer group, and feelings of validation from one another. There was unanimous reporting of high levels of satisfaction with the program by both fellows and supervising staff. Fellows described excitedly anticipating sessions and experiencing renewed energy following group sessions. Supervising staff reported seeing fellows apply skills and strategies learned for prevention and management of STS. Between sessions, fellows reported improved STS symptoms and employing preventative self-care strategies. It was reported that the strategies learned assisted fellows in developing a reflection style that was intentional and individualized for their wellbeing. An unanticipated outcome that was unianimously reported by fellows and noted by staff was the fostering of a strongly bonded and supportive fellowship peer group, further increasing perceptions of wellbeing. Fellows reported feeling validation from one another during group sessions and utilizing approaches in real-time informal discussions with team members as difficult clinical cases arose. CONCLUSION This targeted program for fellows was developed as an innovative approach to addressing secondary traumatic stress among new learners in the challenging field of child maltreatment paediatrics. It has demonstrated acceptability among fellows and supervising staff with reported improvement in STS symptoms. Future steps will address the broader goal of optimizing organizational resilience among other members of the child maltreatment team.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".