A Relational Workforce Capacity Approach to Trauma-Informed Care Implementation: Staff Rejection Sensitivity as a Potential Barrier to Organizational Attachment
Bibliographic record
Abstract
This study explores the relationship between staff rejection sensitivity (a psychological concept grounded in histories of loss and trauma) and organizational attachment among mental health agencies transitioning to Trauma-Informed Care (TIC), which is currently outside the focus of most research. Specifically, this study examines: (1) whether staff rejection sensitivity predicts organizational attachment; (2) whether staff turnover intentions account for the association between rejection sensitivity and organizational attachment; and (3) whether those associations hold once taking into account staff demographic factors (gender, race and ethnicity, education, and income)? Around 180 frontline workers in three Northeastern U.S. mental health agencies responded to surveys collected between 2016 and 2019 using the organizational attachment, rejection sensitivity and turnover intention measures, and their previous TIC training experience. Rejection sensitivity was significantly associated with organizational attachment (β = −0.39, p < 0.001), accounting for 6% of its variance in organizational attachment. The relationship between these variables retained significance, and staff education significantly predicted organizational attachment, with higher education predicting lower levels of organizational attachment (β = −0.15, p < 0.05), accounting for 22% of its variance. This study concludes that TIC transitioning mental health agencies’ staff with a higher rejection sensitivity are more likely to express lower organizational attachment and higher intent-to-turnover.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".