Intervention Proposal: Using Lego in Collaborative Play as an Intervention for Intergenerational Trauma in Chinese-Canadian Families
Bibliographic record
Abstract
Intergenerational trauma is traumatic events experienced by ancestors which passed down through generations of descendants. While there has been published literature on intergenerational trauma, there lies a gap for culturally appropriate interventions for immigrant families that experience this type of trauma. This intervention proposal aims to utilise published research as well as Lego in therapy to bridge the gap in cross-cultural psychology by creating a treatment intervention for immigrant families experiencing intergenerational trauma, more specifically, Chinese immigrant families. Participant criterium are immigrant families with one or more children between the ages of eight to twelve-years-old and can commit to a minimum of 18 consecutive weekly sessions at two hours per week in a supervised therapeutic setting with a trauma-informed mental health professional. Sessions are divided into two interview sessions, 12 pre-determined Lego building tasks with increasing difficulty, and four free-building sessions. The supervising professional will conduct pre- and post- intervention interviews to assess the participants’ baselines, promote constructive and concise communication during sessions, and debriefing with the family after each session. Expected outcomes of this Lego intervention for immigrant families experiencing intergenerational trauma are improved interpersonal communication skills, increased levels of trust, a better recognition of emotions with higher levels of emotion regulation skills, and living in a healthier family environment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".