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Record W4414553275 · doi:10.1002/casp.70180

Adaptation and Implementation of a Trauma‐Informed Approach in a Community Organisation Serving Young Mothers: A Mixed‐Methods Case Study

2025· article· en· W4414553275 on OpenAlexafffund
Marie‐Emma Gagné, Delphine Collin‐Vézina, Rachel Langevin

Bibliographic record

VenueJournal of Community & Applied Social Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAdaptation (eye)Work (physics)Service providerService (business)Plan (archaeology)Burnout

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.489
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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