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Record W4404772645 · doi:10.1080/10522158.2024.2420922

Caregiver engagement and treatment response in child trauma therapy: A qualitative analysis

2024· article· en· W4404772645 on OpenAlex
Sara Lynn Rependa, Laura Goldstein, Karen Fergus, Erica Watson, Janine Lawford, Robert T. Muller

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Family Social Work · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Therapy and Development
Canadian institutionsHospital for Sick ChildrenCentre for Addiction and Mental HealthYork University
Fundersnot available
KeywordsPsychologyQualitative analysisPsychotherapistQualitative researchClinical psychologySociology

Abstract

fetched live from OpenAlex

This study was a qualitative analysis of indicators of caregiver engagement found to impact child response to trauma therapy. There is currently a dearth of research examining the different ways in which parents engage in their child’s trauma therapy as well as how this engagement affects treatment response. The current study used a Task Analysis to explore three “resolved” treatment cases and three “unresolved” cases. Children were between the ages of 6–18 undergoing trauma therapy at a children’s mental health facility in Canada. Cases were qualitatively analyzed for caregiver Positive Engagement Indicators (PEIs) and Negative Engagement Indicators (NEIs). Using an iterative process, four factors were identified as particularly impactful regarding treatment resolution. These factions included the presence of conflict between caregivers, therapists, and children, and low caregiver session attendance. Treatment implications were discussed.

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.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.087
GPT teacher head0.410
Teacher spread0.323 · 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