Bibliographic record
Abstract
Family life is considered to be a context in which children can safely learn life skills such as managing and directing their cognitive, physical, emotional, and behavioral responses to events as a way to achieve a sense of purpose and mastery in life. Traumatic events such as natural disasters, serious accidents, and violence in our homes, schools, or communities may alter an individual’s ability to manage cognitive, physical, emotional, and behavioral functioning. Trauma can significantly affect children and their families, impacting relationships, interactions, and their context. There is evidence to support the use of family therapy with children who have experienced trauma. Family support can improve interactions and relationships and can assist children in resolving trauma symptomology. Trauma-focused cognitive behavioral therapy (TF-CBT) is professionally recognized as an evidence-based intervention. There are also TF-CBT intervention derivations that have been developed to support children experiencing trauma. TF-CBT and TF-CBT intervention derivations are explored in this article including core components of evidence-based trauma-focused family therapy necessary to support traumatized children and their families. Discussion also includes the importance and evidence-based support for honoring cultural diversity and including optimism and hope into family experiences as both a preventative and interventive measure to manage trauma symptomology.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".