Adaptation and Implementation of a Trauma‐Informed Approach in a Community Organisation Serving Young Mothers: A Mixed‐Methods Case Study
Bibliographic record
Abstract
ABSTRACT Community organisations can play a key role in supporting trauma‐impacted young mothers. Trauma‐informed care (TIC) can be vital to support service providers in their work with trauma‐impacted young mothers and can improve the well‐being of all actors within an organisation. This mixed‐methods case study used a collaborative approach to implement TIC within a small community organisation supporting young mothers. It assessed the impact of several TIC implementation strategies (e.g., educational videos, workshops) on service providers' well‐being and attitudes towards TIC ( n = 15), and explored their experiences (qualitative interviews; n = 6) during TIC implementation. Educational videos were associated with changes in the service providers' attitudes towards TIC. An increase in burnout was also observed throughout the TIC implementation that unfortunately occurred during the COVID‐19 pandemic. Overall, informants agreed that TIC influenced the way the organisation conceptualises trauma, but some disagreement remained on the extent of its impact on practice. Reported barriers (e.g., staff turnover, pandemic) helped contextualise the findings. TIC is a promising approach for small community organisations, and several strategies can help facilitate its implementation. However, organisational and contextual challenges were identified, highlighting the need to collaboratively devise a clear and structured plan to maximise TIC implementation efforts.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".