Evaluating the Impact of Providing Training for Direct Care Staff in how to Provide an Emotionally Nurturing Environment for People with Intellectual Disability and Complex Needs
Bibliographic record
Abstract
The service is providing supported living for a number of people, male and female, age 21 to 55, who all have intellectual disability and a range of extra needs. These include physical disability, communication difficulties, trauma related personality difficulties, self-harming behaviour and aggression towards others. They are all perceived to have emotional developmental needs, all having suffered traumatic experiences in early childhood, causing developmental delay. All have difficulties with relationships associated with attachment disorders. All direct support staff are trained in models of emotional development and how to assess the emotional level, which leads to identifying the needs. Managers of services are alto trained to support the staff to provide the level of support needed, and to understand the systemic effects of trauma so as to be able to provide trauma-informed-care. The training, supervision, support and individual therapy for clients, where needed, is provided by psychologists and psychotherapists trained in Disability Psychotherapy. The comprehensive approach allows people with very complex and distressed behaviour to live in ordinary housing in the community. Data is provided on 10 people, the history, the initial problem behaviour and the present position after being provided with the emotionally nurturing 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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".