Collaborating with adults labelled/with intellectual disability to create disability support staff training materials
Bibliographic record
Abstract
Historically, people labelled/with intellectual disability in Canada have received institutionalized forms of care in which they were mistreated, abused, and controlled (Seth et al., 2015; Spagnuolo & Earle, 2017). Today, many people labelled/with intellectual disabilities live within the community and instead receive support from disability support workers in various settings, including within smaller-scale institutions such as group homes, supported independent living arrangements. In some instances, such settings continue to provide institutionalized forms of care (Spagnuolo & Earle, 2017). They may also be in receipt of disability support through involvement with various other community services, including education, employment and recreation. While this shift away from large-scale institutionalization has generally granted a greater level of autonomy for those so labelled than there was previously, the power differential between disability support staff and people labelled/with intellectual disability is such that many problematic support dynamics persist (Sagnuolo & Earle, 2017; Robinson et al., 2022; Antaki et al., 2007). This qualitive co-production project aimed to learn more about what people labelled/with intellectual disability wanted disability support staff to know about the provision of support and did so using a series of focus groups and individual interviews with a participatory component: the co-creation of a series infographics for training of support staff. Thematic analysis revealed two major themes in my data. The first, the ways that support was too often unhelpful or harmful, I broke down into three subthemes: variable treatment, assumptions of (in)capability, and directing or doing for participants leading to neglect of opportunities for skill development. My second theme described what the participants wanted to see from support instead, which also had three sub-themes: respect for boundaries, kind and compassionate treatment, and respect for individuality. My findings and the co-created infographics emphasized the importance of respecting the knowledge that people labelled/with intellectual disabilities have about their own needs, challenging social workers and other professionals to reflect upon their self-perceptions as experts.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".