How We View Our Jobs and Our Clients: A Quantitative Study of Rejection Sensitivity in Trauma-Informed Care
Bibliographic record
Abstract
Despite practice models of trauma-informed care (TIC) emphasizing relational engagement and emotional attunement as critical to service delivery, the role of individual dispositions in shaping staff perceptions and behavior remains underexplored. This study examined how rejection sensitivity, a construct grounded in attachment theory, defined as a dispositional tendency to anxiously expect and overreact to perceived rejection, may influence staff perceptions of their roles and client relationships in residential mental health agencies implementing TIC. We further explored whether individual and organizational factors, including job satisfaction, prior trauma training, perceived isolation at work, and trauma-related knowledge, contribute to these associations. Regression analyses were conducted on survey data from 155 frontline staff across three agencies testing the associations between rejection sensitivity and two relational outcomes: perceptions of work and of clients. Higher rejection sensitivity was significantly associated with more disengaged perceptions of work and less empathic views of clients, even after controlling for demographic and contextual organizational variables. Job satisfaction and trauma knowledge emerged as domain-specific protective factors, reducing the negative impact of rejection sensitivity. The findings underscore the importance of addressing staff relational dispositions to sustain effective TIC implementation. Enhancing job satisfaction and trauma knowledge may help support staff engagement in trauma-informed practice.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".