Trauma-Informed Care in a Homeless Women's Shelter: A Mixed Method Evaluation
Bibliographic record
Abstract
Trauma-informed care (TIC) benefits to service users and providers are increasingly acknowledged across various health and social care settings. TIC can potentially increase service user engagement, prolong shelter placements, and lessen staff vicarious trauma and burnout. However, studies documenting staff experiences and/or the implementation of TIC are scarce. This limitation has prompted calls for more grounded and applied research into trauma-informed practice, tracking implementation in practice, staff perceptions, barriers, and organisational change. This study aims to address this gap and is an ecological, mixed-methods evaluation of the efficacy of TIC training in a female-only homeless shelter. Quantitative data included 132 incident reports during the first yearly quarters pre- and post-training, hypothesising post-training reductions in incident numbers and severity. Using expansive thematic analysis, semi-structured interviews with six shelter staff (n = 6) explored employee views of TIC relative to trauma understanding, incident management, and integration in practice. Findings revealed a marginal increase in incident numbers and a statistically significant reduction in incident severity post-TIC with a 50% reduction in calls to emergency medical services (EMS). Participant accounts of working practice pre- and post-TIC uncovered increased trauma understanding, increased confidence and competence, healing relationships, and enhanced self-care. Findings are discussed with reference to Substance Abuse and Mental Health Services Administration (SAMHSA)’s (2014) trauma-informed framework and Yatchmenoff et al’s. (2017) core questions in evaluating TIC. While these results are significant as one of the first evaluations of TIC training in Ireland, limitations and implications for future research and practice are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".