Integrative Therapy Focused on Trauma for People with Intellectual Disability (TIT-ID): A Therapeutic Answer to Abuse and Intellectual Disability Experience in the Individual and the Family
Bibliographic record
Abstract
Persons with Intellectual Disabilities (ID) have ten times more risk of suffering abuse than persons without ID. When somebody is born with ID, his/her story is printed by trauma of ID (primary trauma). If we add the trauma from disability to the trauma from abuse (secondary trauma), we find very vulnerable population with a high probability of being re-victimised. Victim Support Unit for Persons with Intellectual Disability (UAVDI) proposes an Integrative Therapy focused on Trauma for people with ID (TIT-ID). This therapy is focused on trauma, including the victim and their families and professionals, through different approaches. It intervenes from individual pathoplasty, taking into account side effects caused by abuse. It also works from a systemic perspective of the primary trauma due to ID and primary grief in the individual and his family. It includes a person-centre intervention with attachment theory and organised through phases from theory of structural dissociation. It is very important to do a rigorous analysis of variables involved in the impact of grief (primary trauma) and later in the impact of abuse experience (secondary trauma). The goals of therapy will be planned according to the individual diagnosis. The cross-cutting objectives are the establishment of consistent links to enable the person to restore their feelings of security and sense of self-worth, and also the development of a resilient personality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".